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β2‐microglobulin (β2‐m) is a plasma protein derived from physiological shedding of the class I major histocompatibility complex (MHCI), causing human systemic amyloidosis either due to persistently high concentrations of the wild‐type (WT) protein in hemodialyzed patients, or in presence of mutations, such as D76N β2‐m, which favor protein deposition in the adulthood, despite normal plasma levels. Here we describe a new transgenic Caenorhabditis elegans (C. elegans) strain expressing human WT β2‐m at high concentrations, mimicking the condition that underlies dialysis‐related amyloidosis (DRA) and we compare it to a previously established strain expressing the highly amyloidogenic D76N β2‐m at lower concentrations. Both strains exhibit behavioral defects, the severity of which correlates with β2‐m levels rather than with the presence of mutations, being more pronounced in WT β2‐m worms. β2‐m expression also has a deep impact on the nematodes' proteomic and metabolic profiles. Most significantly affected processes include protein degradation and stress response, amino acids metabolism, and bioenergetics. Molecular alterations are more pronounced in worms expressing WT β2‐m at high concentration compared to D76N β2‐m worms. Altogether, these data show that β2‐m is a proteotoxic protein in vivo also in its wild‐type form, and that concentration plays a key role in modulating pathogenicity. Our transgenic nematodes recapitulate the distinctive features subtending DRA compared to hereditary β2‐m amyloidosis (high levels of non‐mutated β2‐m vs. normal levels of variant β2‐m) and provide important clues on the molecular bases of these human diseases. [ABSTRACT FROM AUTHOR]

Background: Contradictory results were reported on the role of school closure/reopening on the overall SARS-CoV-2 transmission rate, as well as on which kind and level of mitigation measures implemented in schools may be effective in limiting its diffusion. Some recent studies were reassuring, showing that opening did not increase the community spread, although teachers and families are worried about the high class density. On the other hand, distance learning was associated with a negative impact on learning, sociability and psychological health, especially in vulnerable children. As it becomes clear that the SARS-CoV-2 pandemic will last for a long time, there is a high need for studies and solutions to support safe schools opening based on scientific evidence of harms and benefits. The Lolli-Methode (LM) is a strategy for epidemiological surveillance and early intervention aiming at SARS-CoV-2 outbreaks' reduction in schools, relying on polymerase chain reaction analysis of saliva samples. Methods: In this cluster randomised trial protocol, we aim to determine whether the LM is useful to support schools opening and to reduce clusters and attack rates in schools, compared with the standard of care (SoC) surveillance by public health departments. This multicenter study will enrol 440 classes (around 8800 students, teachers and other personnel) from two countries, cluster randomised to LM or SoC. The samples from the pools will be collected and tested using PCR-based techniques. Test results will be combined with questionnaires filled in by children, parents, schoolteachers, and principals, concerning ongoing mitigation measures, their perceived psychological impact and other health and socio-economic information. An ancillary observational study will be carried out to study the prevalence of SARS-CoV-2 in schools, frequencies and size of clusters and attack rates, to compare the effectiveness of the different preventive measures adopted and to evaluate psychological issues in students and teachers in relation to the pandemic's containment measures. Discussion : By the end of this study, we will have defined and characterised the applicability of the LM for SARS-CoV-2 surveillance, as well as the impact of pandemic preventive measures on children and teachers. Trial registration International Standard Randomised Controlled Trial Number: NCT05396040, 27.05.2022. [ABSTRACT FROM AUTHOR]

Background: Contradictory results were reported on the role of school closure/reopening on the overall SARS-CoV-2 transmission rate, as well as on which kind and level of mitigation measures implemented in schools may be effective in limiting its diffusion. Some recent studies were reassuring, showing that opening did not increase the community spread, although teachers and families are worried about the high class density. On the other hand, distance learning was associated with a negative impact on learning, sociability and psychological health, especially in vulnerable children. As it becomes clear that the SARS-CoV-2 pandemic will last for a long time, there is a high need for studies and solutions to support safe schools opening based on scientific evidence of harms and benefits. The Lolli-Methode (LM) is a strategy for epidemiological surveillance and early intervention aiming at SARS-CoV-2 outbreaks' reduction in schools, relying on polymerase chain reaction analysis of saliva samples. Methods: In this cluster randomised trial protocol, we aim to determine whether the LM is useful to support schools opening and to reduce clusters and attack rates in schools, compared with the standard of care (SoC) surveillance by public health departments. This multicenter study will enrol 440 classes (around 8800 students, teachers and other personnel) from two countries, cluster randomised to LM or SoC. The samples from the pools will be collected and tested using PCR-based techniques. Test results will be combined with questionnaires filled in by children, parents, schoolteachers, and principals, concerning ongoing mitigation measures, their perceived psychological impact and other health and socio-economic information. An ancillary observational study will be carried out to study the prevalence of SARS-CoV-2 in schools, frequencies and size of clusters and attack rates, to compare the effectiveness of the different preventive measures adopted and to evaluate psychological issues in students and teachers in relation to the pandemic's containment measures. Discussion : By the end of this study, we will have defined and characterised the applicability of the LM for SARS-CoV-2 surveillance, as well as the impact of pandemic preventive measures on children and teachers. Trial registration International Standard Randomised Controlled Trial Number: NCT05396040, 27.05.2022. [ABSTRACT FROM AUTHOR]

The relationship between physical exercise and improvement in specific cognitive domains in children and adolescents who play sport has been recently reported, although the effects on visuospatial abilities have not yet been well explored. This study is aimed at evaluating in school-age children practicing artistic gymnastics the visuospatial memory by using a table version of the Radial Arm Maze (table-RAM) and comparing their performances with those ones who do not play any sport. The visuospatial performances of 14 preadolescent girls practicing artistic gymnastics aged between 7 and 10 years and those of 14 preadolescent girls not playing any sport were evaluated in the table-RAM forced-choice paradigm that allows disentangling short-term memory from working memory abilities. Data showed that the gymnasts obtained better performances than control group mainly in the parameters evaluating working memory abilities, such as within-phase errors and spatial span. Our findings emphasizing the role of physical activity on cognitive performances impel to promote physical exercise in educational and recreational contexts as well as to analyse the impact of other sports besides gymnastics on cognitive functioning. [ABSTRACT FROM AUTHOR]

Systemic amyloidosis caused by extracellular deposition of insoluble fibrils derived from the pathological aggregation of circulating proteins, such as transthyretin, is a severe and usually fatal condition. Elucidation of the molecular pathogenic mechanism of the disease and discovery of effective therapies still represents a challenging medical issue. The in vitro preparation of amyloid fibrils that exhibit structural and biochemical properties closely similar to those of natural fibrils is central to improving our understanding of the biophysical basis of amyloid formation in vivo and may offer an important tool for drug discovery. Here, we compared the morphology and thermodynamic stability of natural transthyretin fibrils with those of fibrils generated in vitro either using the common acidification procedure or primed by limited selective cleavage by plasmin. The free energies for fibril formation were -12.36, -8.10, and -10.61 kcal mol-1, respectively. The fibrils generated via plasmin cleavage were more stable than those prepared at low pH and were thermodynamically and morphologically similar to natural fibrils extracted from human amyloidotic tissue. Determination of thermodynamic stability is an important tool that is complementary to other methods of structural comparison between ex vivo fibrils and fibrils generated in vitro. Our finding that fibrils created via an in vitro amyloidogenic pathway are structurally similar to ex vivo human amyloid fibrils does not necessarily establish that the fibrillogenic pathway is the same for both, but it narrows the current knowledge gap between in vitro models and in vivo pathophysiology. [ABSTRACT FROM AUTHOR]

The worldwide incidence of melanoma has increased rapidly over the last 50 years. Melanoma is the most common cancer found in the young adult population, and its incidence is very high among geriatric populations. The incidence of melanoma varies by sex, and this factor is also associated with differences in the anatomical site melanoma. Adolescent and young adult women have a higher incidence than men. This may be, in part, due to the greater use of sunbeds, as well as intentional sun exposure among girls and, in general, risky behaviours in seeking to suntan, due to sociallydetermined aesthetic needs. Indeed, the World Health Organization declared that there is sufficient evidence to classify exposure to ultraviolet radiation (sunbed use and sun exposure) as carcinogenic to humans. Although pigmentation characteristics, such as skin colour, hair and eye colour, freckles and number of common and atypical naevi, do influence susceptibility to melanoma, recommendations regarding prevention should be directed to the entire population and should include avoiding sunbed, covering sun-exposed skin, wearing a hat and sunglasses. Sunscreen use should not be used to prolong intentional sun exposure. Primary prevention should be focused mainly on young adult women, while secondary prevention should be focused mainly on elderly men. In fact, after the age of 40 years, incidence rates reverse, and the incidence of melanoma among men is greater than among women. This is probably due to the fact that men are less likely than women to examine their own skin or present to a dermatologist for skin examination. [ABSTRACT FROM AUTHOR]

The availability of a genetic model organism with which to study key molecular events underlying amyloidogenesis is crucial for elucidating the mechanism of the disease and the exploration of new therapeutic avenues. The natural human variant of β2-microglobulin (D76N β2-m) is associated with a fatal familial form of systemic amyloidosis. Hitherto, no animal model has been available for studying in vivo the pathogenicity of this protein. We have established a transgenic C. elegans line, expressing the human D76N β2-m variant. Using the INVertebrate Automated Phenotyping Platform (INVAPP) and the algorithm Paragon, we were able to detect growth and motility impairment in D76N β2-m expressing worms. We also demonstrated the specificity of the β2-m variant in determining the pathological phenotype by rescuing the wild type phenotype when β2-m expression was inhibited by RNA interference (RNAi). Using this model, we have confirmed the efficacy of doxycycline, an inhibitor of the aggregation of amyloidogenic proteins, in rescuing the phenotype. In future, this C. elegans model, in conjunction with the INVAPP/Paragon system, offers the prospect of high-throughput chemical screening in the search for new drug candidates. [ABSTRACT FROM AUTHOR]

Reference 1 Raimondi S, Gandini S, Rubio Quintanares GH, Abecasis A, Lopalco PL, D'Ecclesiis O, Chiocca S, Tomezzoli E, Cutica I, Mazzoni D, Amparo N, Pingarilho M, Carmagnola D, Dellavia C, Zuccotti G, Ronchini C, Bellerba F, Dewald F, Kaiser R, Incardona FThe Eucare WP4. Sara Raimondi and Sara Gandini are co-first author B Correction b B : b B BMC Infectious Diseases (2023) 23:1 b https://doi.org/10.1186/s12879-022-07947-6 Following publication of the original article [[1]], the authors identified an error in the author name of Claudia Dellavia. [Extracted from the article]

Objective: To validate previously identified associations between radiological features and clinical features with Epidermal Growth Factor Receptor (EGFR)/ Kirsten RAt Sarcoma (KRAS) alterations in an independent group of patients with Non-Small Cell Lung Cancer (NSCLC).Material and Methods: A total of 122 patients with NSCLC tested for EGFR/KRAS alterations were included. Clinical and radiological features were recorded. Univariate analysis were performed to look at the associations of the studied features with EGFR/KRAS alterations. Previously calculated composite model parameters for each gene alteration prediction were applied to this validation cohort. ROC (Receiver Operating Characteristic) curves were drawn using the previously validated composite models, and also for each significant individual characteristic of the previous training cohort model. The Area Under the ROC Curve (AUC) with 95% Confidence Intervals (CI) was calculated and compared between the full models.Results: At univariate analysis, EGFR+ confirmed an association with an internal air bronchogram, pleural retraction, emphysema and lack of smoking; KRAS+ with round shape, emphysema and smoking. The AUC (95%CI) in the new cohort was confirmed to be high for EGFR+ prediction, with a value of: 0.82 (0.69-0.95) vs. 0.82 in the previous cohort, whereas it was smaller for KRAS+ prediction, with a value of 0.60 (0.48-0.72) vs. 0.67 in the previous cohort. Looking at single features in the new cohort, we found that the AUC for the models including only smoking was similar to that of the full model (including radiological and clinical features) for both gene alterations.Conclusions: Although this study validated the significant association of clinical and radiological features with EGFR/KRAS alterations, models based on these composite features are not superior to smoking history alone to predict the mutations. [ABSTRACT FROM AUTHOR]

There is much evidence supporting the role of telomeres in cancer pathogenesis, however the studies that investigated the association between telomere length and skin cancer risk provided inconsistent results. To help clarify this issue, we performed a systematic review and meta-analysis of published papers on the association between peripheral leukocytes telomere length (PLTL) and the risk of cutaneous melanoma and non-melanoma skin cancer (NMSC). We calculated summary relative risks (SRR) and 95% confidence intervals (95%CI) using random effect models with maximum likelihood estimates, and explored causes of between-studies heterogeneity of risk estimates. We included 1629 cutaneous melanoma and 1439 NMSC from eight independent studies published until March 2015. The SRR of cutaneous melanoma for those in the lowest (vs. highest) category of PLTL distribution was 0.25 (95% CI 0.09–0.67). The results were less clear for NMSC, with two studies reporting no association and one study showing an increase in risk for those in the lowest (vs. highest) category of PLTL distribution. For both cutaneous melanoma and NMSC, the between-studies heterogeneity was large, mainly due to inclusion of hospital-based case-control studies. Our meta-analysis shows evidence of an association between short PLTL and reduced risk for cutaneous melanoma. [ABSTRACT FROM AUTHOR]

The VDR gene is an important regulator of the vitamin D pathway, and the role of some of its polymorphisms on cancer risk was previously investigated. A trend of cancer risk reduction with the VDR BsmI B allele was observed for many cancer sites. We performed a comprehensive meta-analysis to investigate the role of VDR BsmI polymorphism on cancer risk, even according to different ethnicities. Summary odds ratios (SORs) were calculated with random-effects models and maximum likelihood estimation. We categorized studies into three groups (“moderate”, “high” and “very high confidence”) according to departure from Hardy–Weinberg equilibrium in controls, reported minor allele frequency and genotyping quality controls. The meta-analysis included 73 studies with 45,218 cases and 52,057 controls. We found a significant 6–7% reduction of cancer risk at any site respectively for carriers of Bb genotype (SOR; 95%CI: 0.94; 0.90–0.99) and for carriers of BsmI BB genotype (SOR; 95%CI: 0.93; 0.89–0.98) compared to bb carriers, and they remain statistically significant when we restricted the analysis to at least “high confidence” studies. For skin cancer, a significant risk reduction was observed for Bb carriers (SOR; 95%CI: 0.86; 0.76–0.98). We also found a significant reduction of colorectal cancer risk for BB and Bb + BB genotypes carriers, but these SORs were no more significant when we restricted the analysis to studies with “high confidence”. When the analysis was stratified by ethnicity, we still observed a significant decreased risk for both Bb and BB compared to bb genotype among Caucasians: SORs (95%CI) for any cancer site were 0.97 (0.93–1.00) and 0.95 (0.91–0.99), respectively. Among other ethnic groups the inverse association was still present, but did not reach statistical significance. In conclusion, we suggest a weak effect of BsmI B allele in reducing cancer risk at any site, especially of the skin. [ABSTRACT FROM AUTHOR]

Background: For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. Design and methods: Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling. Discussion: Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields. [ABSTRACT FROM AUTHOR]

Abstract: The 93-residue N-terminal fragment of apolipoprotein A-I (ApoA-I) is the major constituent of fibrils isolated from patients affected by the amyloidosis caused by ApoA-I mutations. We have prepared eight polypeptides corresponding to all the currently known amyloidogenic variants of the N-terminal region of ApoA-I, other than a truncation mutation, and investigated their aggregation kinetics and the associated structural modifications. All the variants adopted a monomeric highly disordered structure in solution at neutral pH, whereas acidification of the solution induced an unstable α-helical conformation and the subsequent aggregation into the cross-β structure aggregate. Two mutations (Δ70–72 and L90P) almost abrogated the lag phase of the aggregation process, three mutations (Δ60–71, L75P, and W50R) significantly accelerated the aggregation rate by 2- to 3-fold, while the remaining three variants (L64P, L60R, and G26R) were not significantly different from the wild type. Therefore, an increase in aggregation propensity cannot explain per se the mechanism of the disease for all the variants. Prediction of the protection factors for hydrogen exchange in the native state of full-length protein reveals, in almost all the variants, an expansion of the conformational fluctuations that could favour the proteolytic cleavage and the release of the amyloidogenic peptide. Such an event seems to be a necessary prerequisite for ApoA-I fibrillogenesis in vivo, but the observed increased aggregation propensity of certain variants can have a strong influence on the severity of the disease, such as an earlier onset and a faster progression. [Copyright &y& Elsevier]

The discovery of methods suitable for the conversion in vitro of native proteins into amyloid fibrils has shed light on the molecular basis of amyloidosis and has provided fundamental tools for drug discovery. We have studied the capacity of a small library of tetracycline analogues to modulate the formation or destructuration of β2-microglobulin fibrils. The inhibition of fibrillogenesis of the wild type protein was first established in the presence of 20% trifluoroethanol and confirmed under a more physiologic environment including heparin and collagen. The latter conditions were also used to study the highly amyloidogenic variant, P32G. The NMR analysis showed that doxycycline inhibits β2-microglobulin self-association and stabilizes the native-like species through fast exchange interactions involving specific regions of the protein. Cell viability assays demonstrated that the drug abolishes the natural cytotoxic activity of soluble β2-microglobulin, further strengthening a possible in vivo therapeutic exploitation of this drug. Doxycycline can disassemble preformed fibrils, but the IC50 is 5-fold higher than that necessary for the inhibition of fibrillogenesis. Fibril destructuration is a dynamic and time- dependent process characterized by the early formation of cytotoxic protein aggregates that, in a few hours, convert into non-toxic insoluble material. The efficacy of doxycycline as a drug against dialysis-related amyloidosis would benefit from the ability of the drug to accumulate just in the skeletal system where ainyloid is formed. In these tissues, the doxycycline concentration reaches values several folds higher than those resulting in inhibition of amyloidogenesis and amyloid destructuration in vitro. [ABSTRACT FROM AUTHOR]

Background: We have recently discovered that the two tryptophans of human β2-microglobulin have distinctive roles within the structure and function of the protein. Deeply buried in the core, Trp95 is essential for folding stability, whereas Trp60, which is solvent-exposed, plays a crucial role in promoting the binding of β2- microglobulin to the heavy chain of the class I major histocompatibility complex (MHCI). We have previously shown that the thermodynamic disadvantage of having Trp60 exposed on the surface is counter-balanced by the perfect fit between it and a cavity within the MHCI heavy chain that contributes significantly to the functional stabilization of the MHCI. Therefore, based on the peculiar differences of the two tryptophans, we have analysed the evolution of β2-microglobulin with respect to these residues. Results: Having defined the β2-microglobulin protein family, we performed multiple sequence alignments and analysed the residue conservation in homologous proteins to generate a phylogenetic tree. Our results indicate that Trp60 is highly conserved, whereas some species have a Leu in position 95; the replacement of Trp95 with Leu destabilizes β2-microglobulin by 1 kcal/mol and accelerates the kinetics of unfolding. Both thermodynamic and kinetic data fit with the crystallographic structure of the Trp95Leu variant, which shows how the hydrophobic cavity of the wild-type protein is completely occupied by Trp95, but is only half filled by Leu95. Conclusions: We have established that the functional Trp60 has been present within the sequence of β2- microglobulin since the evolutionary appearance of proteins responsible for acquired immunity, whereas the structural Trp95 was selected and stabilized, most likely, for its capacity to fully occupy an internal cavity of the protein thereby creating a better stabilization of its folded state. [ABSTRACT FROM AUTHOR]

Background. A Joint Expert Panel recently published guidelines for adjuvant cisplatin-based chemotherapy, recommending routine use in patients with completely resected stage II (T1-2N1 and T3N0) non-small-cell lung cancer (NSCLC). However, these two tumor subgroups should be considered as different entities. While the effi cacy of adjuvant chemotherapy has been established in patients with T1-2N1 NSCLC, its benefi t in patients with T3N0 tumor remains questionable. Material and methods. We performed an extensive review of the literature using the Joint Expert Panel guidelines as a start point. Altogether, we identifi ed 76 potentially relevant articles. Basing on inclusion and exclusion criteria, 23 of the 76 articles were eventually included in this review. Results. After careful evaluation of the selected articles, we found no information on the effect of adjuvant chemotherapy in patients with T3N0 NSCLC. Discussion. In the absence of evidencebased data, we recommend that the lack of information on the effi cacy of adjuvant chemotherapy for T3N0 tumors be discussed with patients and propose chemotherapy as an individual option. While the effi cacy of adjuvant chemotherapy will be diffi cult to assess prospectively through a large randomized clinical trial, a pooled-analysis of the existing data would quickly and with a limited effort provide a preliminary answer. [ABSTRACT FROM AUTHOR]

Recently released clinical practice guidelines and consensus conference statements point to the importance of hepatitis B virus (HBV) genotyping in therapeutic algorithms for the treatment of chronic hepatitis B. This information usually comes from post hoc analyses of clinical trials which were not designed to study associations with the HBV genotype. We have performed a literature search through to April 2009 and have selected randomized clinical trials of currently approved anti-HBV drugs providing information on HBV genotypes and (i) baseline characteristics of study subjects, (ii) any response to antiviral therapy, (iii) interaction between HBV genotypes and the type of therapy. There were several intrinsic features and weaknesses in the majority of clinical trials conducted so far which make it difficult to reach firm conclusions about the role of HBV genotypes in response to antiviral therapy. Indeed, most trials were necessarily multicenter in order to reach a sufficient statistical power, but pooling together patients of different ethnicities may have revealed false-positive associations between response to antiviral therapy and HBV genotype. Moreover, endpoint definitions, especially for the composite ones, varied substantially among studies, leading to lack of homogeneity. Finally, possible interactions between the type of therapy and the HBV genotype were only seldom analysed. The present review highlights several caveats regarding current indications proposed by the major clinical practice guidelines and consensus conference statements published thus far and emphasise the need for further long term studies in the field. [Copyright &y& Elsevier]

Abstract: The etiology of colorectal cancer is complex and multifactorial. Tobacco smoke has been found to be associated both with colorectal adenoma and cancer development. It was hypothesized that tobacco smoking could interact with genetic factors, providing different risk estimates according to different genetic predisposition. We reviewed and summarized by a meta-analytic approach the evidence from the literature on the interaction of smoking with the five most studied gene polymorphisms (GSTM1, GSTT1, mEH3, mEH4, NAT2). We calculated pooled Odds Ratios for each gene–smoking combination by random effects models, and provided a pooled P-value for gene–smoking interaction. Heterogeneity among studies was evaluated by the Q statistic and I 2. Overall, 27 case–control studies or nested case–control studies are included in this review: 12 presented data on GSTM1 polymorphism, eight on GSTT1, seven on mEH3, mEH4, and 10 on NAT2. We found a weak suggestion of an antagonistic effect of mEH3 low or medium metabolizer with smoking on colorectal adenoma risk (pooled P-value for the interaction: 0.02): smokers carriers of mEH3 low or medium metabolizer had slightly lower risk (Odds Ratio; 95% Confidence Interval: 1.6; 1.2–2.1) than smokers with mEH3 high metabolizer (1.8; 1.4–2.4). A non-significant positive interaction between GSTT1 null genotype and smoking was suggested for colorectal adenoma risk. None of the other common genetic polymorphisms involved in tobacco carcinogens metabolism seemed to modify the smoking-related risk of colorectal adenoma or cancer. [Copyright &y& Elsevier]

Deposition of amyloid in the buttock is a rare complication of dialysis related amyloidosis (DRA), but this localization is even rarer in other types of amyloidoses. We report here the clinical, radiological, and biochemical features of a patient who incurred into this complication after 27 years of hemodialysis. Imaging of the amyloid deposition by magnetic resonance imaging (MRI) documents the amyloid infiltration in the muscles of the buttock region and highlights a peculiar feature of amyloid fibrils deposition in the subcutaneous fat. The amyloid deposition is confirmed by biochemical and microscopic analysis of fibrils extracted from a biopsy specimen. Review of literature and the features of this case lead to speculation that the peculiar involvement of the buttock region including muscles and subcutaneous fat in DRA might derive from the propagation of amyloid initially deposited in the hip joint. [ABSTRACT FROM AUTHOR]

Background/Aims: Hepatitis C virus (HCV) is a known risk factor for hepatocellular carcinoma (HCC), but whether the risk varies among patients infected with different HCV genotypes is still controversial. We performed a meta-analysis to clarify whether the genotype 1b is associated with a higher risk of HCC than other genotypes. Methods: We identified 57 relevant papers through a literature search to December 2007 but, since age could represent a major confounder, we focused the meta-analysis on the 21 studies presenting age-adjusted risk estimates for HCV genotype 1b vs. other genotypes. We used random-effects models with the DerSimonian–Laird method and assessed heterogeneity between studies and publication bias. Results: Patients infected with HCV genotype 1b have almost double the risk to develop HCC than those infected with other genotypes (Relative Risk (95% Confidence Intervals)=1.78(1.36–2.32)). The pooled risk estimate was somewhat lower when we restricted the analysis to the eight studies conducted in patients with liver cirrhosis (1.60;1.07–2.39) or considering the 36 studies presenting only crude data (1.63;1.30–2.06). In seven studies excluding patients with liver cirrhosis, the RR (95% CI) increased to 2.46(1.69–3.59). Conclusions: This meta-analysis suggests that HCV genotype 1b plays an important role in HCC development, especially in patients with early stage liver disease. [Copyright &y& Elsevier]

Abstract: Beta-2 microglobulin (β2m) is the light chain of class I major histocompatibility complex (MHC-I). β2m is an intrinsically amyloidogenic protein that can assemble into amyloid fibrils in a concentration dependent manner. β2m is accumulated in serum of haemodialysed patients, and deposited in the skeletal joints, causing dialysis related amyloidosis. Recent reports suggested that the loop comprised between β2m strands D and E is crucial for protein stability and for β2m propensity to aggregate as cross-β structured fibrils. In particular, the role of Trp60 for β2m stability has been highlighted by showing that the Trp60→Gly β2m mutant is more thermo-stable and less prone to aggregation than the wild type protein. On the contrary the Asp59→Pro β2m mutant shows lower Tm and stronger tendency to fibril aggregation. To further analyse such properties, the Trp60→Val β2m mutant has been expressed and purified; the propensity to fibrillar aggregation and the folding stability have been assessed, and the X-ray crystal structure determined to 1.8Å resolution. The W60V mutant structural features are discussed, focusing on the roles of the DE loop and of residue 60 in relation to β2m structure and its amyloid aggregation trends. [Copyright &y& Elsevier]

Abstract: A comprehensive bibliographic search of the literature was conducted to identify studies on Cutaneous Malignant Melanoma (CMM) and non-melanoma skin cancer (NMSC), Vitamin D receptor (VDR) polymorphisms, Vitamin D intake and 25(OH)D serum levels. Fully adjusted risk estimates were found and extracted for the two polymorphisms FokI and BsmI and Vitamin D intake. Ten studies were included in the meta-analysis, with a total of 6805 skin cancer cases. We found an association with CMM for both polymorphisms. The summary relative risks (SRR) for the studies on CMM were: 1.21 (1.03–1.42) and 1.21 (0.95–1.54) for the Ff and ff versus wild-type of FokI, respectively. The SRR for ff versus wild-type became significant with the inclusion of NMSC. The SRR for the studies on CMM were: 0.78 (0.65–0.92) and 0.75 (0.59–0.95) for the Bb and BB versus wild-type of BsmI, respectively. There is also a slight indication of a role of dietary Vitamin D in CMM development. In conclusion, this meta-analysis suggests a possible significant role of VDR FokI and BsmI polymorphism in CMM and NMSC risk. The association with Vitamin D intake is less clear and further studies could be useful to clarify the role of diet. [Copyright &y& Elsevier]

Abstract: Modification of DNA is believed to be a key step in carcinogenesis, and therefore DNA adducts have been proposed as predictive biomarkers of human cancer. Smoked and grilled foods are important contributors of PAH-DNA adduct levels, while the consumption of flavonoids and other antioxidants seems to decrease the level of DNA adducts. The aim of this study was to assess the effect of each group of foods and of different dietary profiles on the DNA adducts levels and on oxidative damage to DNA. Occupationally exposed men were recruited in Czech Republic, Slovak Republic and Bulgaria. Non-occupationally exposed subjects were matched on age and gender to PAH-exposed workers. Three hundred and fifty-six subjects who completed the questionnaire for dietary information and had a measurement of DNA adduct levels and oxidative damage to DNA were included in this study. No food item seemed to be individually associated with markers of exposure or DNA damage. Total DNA adducts levels were significantly higher for subjects who had eaten, in the previous 24h, smoked or fried food. A Principal Components Analysis was performed to identify groups of subjects with similar dietary profiles: no significant differences in biomarker levels were observed among the groups defined according to dietary profiles. In conclusion, this study did not show any significant association between diet and biomarkers of DNA damage, oxidative damage to DNA and chromosomal aberrations, neither when each food was considered separately, nor when the effect of different dietary profiles was tested. The recent consumption of smoked or fried food was associated with an increase in total DNA adducts levels. [Copyright &y& Elsevier]

A cohort of children born after assisted reproductive technologies (ART) was set up in 2003 in Italy. It aims to follow up the children in order to study the short- and long-term effects of ART. Parents who agreed to participate were contacted for a telephone interview; questions included occupational and non-occupational exposure to carcinogens, reproductive history, history of index pregnancy (including drugs used during pregnancy), delivery and the child’s health status. By August 2005, 40 out of the 50 centres contacted (80%) had agreed to participate in the study, and 17 had already sent their data. Information on a total of 2451 cycles ending with a pregnancy are currently available, from 2245 couples. We have contacted 351 of these couples (16%), 309 of whom (88% of the contacts) agreed to participate in the study and were interviewed, while 36 (11%) refused to be interviewed. The total number of children currently included in the database is 411. This study is the first attempt to create a database containing information on children born after ART in Italy. It will provide results on both short- and long-term outcomes in these children. [ABSTRACT FROM AUTHOR]

Abstract: Background: Since genetic factors may play an important role in lung cancer development at low dose carcinogen exposure, non-smokers are a good model to study genetic susceptibility and its interaction with environmental factors. Materials and methods: We evaluated the role of the metabolic gene polymorphisms CYP1A1MspI, CYP1A1Ile 462 Val, GSTM1, and GSTT1 in non-smoker lung cancer patients from the International Collaborative Study on Genetic Susceptibility to Environmental Carcinogens (GSEC). Non-smokers (defined as subjects who never smoked on a regular basis) were selected from the GSEC database. We pooled the raw data from 21 case-control studies for a total of 2764 Caucasians (555 cases and 2209 controls) and 383 Asians (113 cases and 270 controls). Tests of heterogeneity and of inclusion bias were performed. Results: A significant association between lung cancer and CYP1A1Ile 462 Val polymorphism was observed in Caucasians (adjusted OR=2.04, 95% CI 1.17–3.54). GSTT1 deletion seems to be a risk factor for lung cancer in Caucasian non smokers only when the analysis was restricted to studies including healthy controls (adjusted OR=1.66, 95% CI 1.12–2.46). A protective effect on lung cancer was observed with the combination of CYP1A1 wild type, GSTM1 null, and GSTT1 non-null genotypes. None of the analysed polymorphisms were associated with lung cancer in Asian non-smokers. Discussion: Our analysis confirms previous findings that CYP1A1Ile 462 Val polymorphism may play a role in lung carcinogenesis in Caucasian non-smokers. [Copyright &y& Elsevier]

AL amyloidosis is a life-threatening disease caused by deposition of immunoglobulin light chains. While the mechanisms underlying light chains amyloidogenesis in vivo remain unclear, several studies have highlighted the role that tissue environment and structural amyloidogenicity of individual light chains have in the disease pathogenesis. AL natural deposits contain both full-length light chains and fragments encompassing the variable domain (VL) as well as different length segments of the constant region (CL), thus highlighting the relevance that proteolysis may have in the fibrillogenesis pathway. Here, we investigate the role of major truncated species of the diseaseassociated AL55 light chain that were previously identified in natural deposits. Specifically, we study structure, molecular dynamics, thermal stability, and capacity to form fibrils of a fragment containing both the VL and part of the CL (133-AL55), in comparison with the full-length protein and its variable domain alone, under shear stress and physiological conditions. Whereas the full-length light chain forms exclusively amorphous aggregates, both fragments generate fibrils, although, with different kinetics, aggregate structure, and interplay with the unfragmented protein. More specifically, the VL-CL 133- AL55 fragment entirely converts into amyloid fibrils microscopically and spectroscopically similar to their ex vivo counterpart and increases the amorphous aggregation of full-length AL55. Overall, our data support the idea that light chain structure and proteolysis are both relevant for amyloidogenesis in vivo and provide a novel biocompatible model of light chain fibrillogenesis suitable for future mechanistic studies. [ABSTRACT FROM AUTHOR]

• VDR polymorphisms have been found to be associated with some cancer sites. • Cancer incidence rates vary by ethnicity and genetic factors. • Fok1, Bsm1, Cdx2, Apa1 and Taq1 polymorphisms are linked to cancer risk. • Ethnicity may be a modifier of risk in particular for hormone dependent cancers. Vitamin D receptors polymorphisms are found to be associated with several cancers. Since their prevalence vary across ethnicities and ethnicity itself seems to influence the cancer risk, a comprehensive meta-analysis was performed to investigate the role of VDR Fok1, Bsm1, Taq1, Apa1, Cdx2 and cancer risk at specific organ sites. Odds ratios, calculated with random-effects models, summarized one-hundred-ninety-two independent studies for twenty-two cancer sites. Evidence was provided that Fok1 , Bsm1 , Cdx2 , Apa1 and Taq1 are linked to cancer susceptibility for colorectal, lung, ovarian, skin, multiple myeloma and brain cancer. Stratifying by ethnicity, some differences were found, partially explained by minor allele frequency (MAF), for colorectal cancer, ovarian and prostate cancer in Caucasian and prostate cancer in Asian populations. In summary, ethnicity may be a modifier of cancer risk, in particular for hormone dependent cancers and it should be considered evaluating the effect of VDR on cancer risk. [ABSTRACT FROM AUTHOR]

Background: To evaluate whether a model based on radiomic and clinical features may be associated with lymph node (LN) status and overall survival (OS) in lung cancer (LC) patients; to evaluate whether CT reconstruction algorithms may influence the model performance. Methods: patients operated on for LC with a pathological stage up to T3N1 were retrospectively selected and divided into training and validation sets. For the prediction of positive LNs and OS, the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was used; univariable and multivariable logistic regression analysis assessed the association of clinical-radiomic variables and endpoints. All tests were repeated after dividing the groups according to the CT reconstruction algorithm. p-values < 0.05 were considered significant. Results: 270 patients were included and divided into training (n = 180) and validation sets (n = 90). Transfissural extension was significantly associated with positive LNs. For OS prediction, high- and low-risk groups were different according to the radiomics score, also after dividing the two groups according to reconstruction algorithms. Conclusions: a combined clinical–radiomics model was not superior to a single clinical or single radiomics model to predict positive LNs. A radiomics model was able to separate high-risk and low-risk patients for OS; CTs reconstructed with Iterative Reconstructions (IR) algorithm showed the best model performance. [ABSTRACT FROM AUTHOR]

Comments on research by Montserrat Garcí-Closas and colleagues that suggests a genetic susceptibility to bladder cancer. Idea that the risk is associated with aromatic amines from occupational exposure and smoking; The role of glutathione S-transferase M1 (GSTM1) and NAT2 acetylator genotype interactions in assessing cancer risk; Data showing the association between various types of cancer and genetic polymorphisms.

• Breast screening needs a critical balance of early detection and risk of overtreatment. • AI integration elevates diagnostic precision in breast cancer screening. • Emphasis on personalized approaches in screening mitigate risks of overtreatment. • Emerging imaging technologies may significantly improve early cancer detection. • Ensuring equitable access to breast cancer screening is vital for diverse populations. Breast cancer remains a significant global health challenge, with projections indicating a troubling increase in incidence. Breast cancer screening programs have long been hailed as life-saving initiatives, yet their true impact on mortality rates is a subject of ongoing debate. Screening poses the risk of false positives and the detection of indolent tumors, potentially leading to overtreatment. Bias factors, including lead time, length time, and selection biases, further complicate the assessment of screening efficacy. Recent studies suggest that AI-driven image analysis may revolutionize breast cancer screening, maintaining diagnostic accuracy while reducing radiologists' workload. However, the generalizability of these findings to diverse populations is a critical consideration. Personalized screening approaches and equitable access to advanced technologies are essential to mitigate disparities. In conclusion, the breast cancer screening landscape is evolving, emphasizing the need for risk stratification, appropriate imaging modalities, and a personalized approach to reduce overdiagnosis and focus on cancers with the potential to impact lives while prioritizing patient-centered care. [ABSTRACT FROM AUTHOR]

• No causal impact of schools reopening on the spread of Omicron was found. • Schools did not increase SARS-CoV-2 transmission in Italy, Germany, and Portugal. • Geography, seasons, and population behavior exerted a more substantial influence. • No differences were observed by age group in SARS-CoV-2 transmission rates. • We are reassured of the involvement of schools, which is crucial during a pandemic. We investigated the impact of school reopening on SARS-CoV-2 transmission in Italy, Germany, and Portugal in autumn 2022 when the Omicron variant was prevalent. A prospective international study was conducted using the case reproduction number (R c) calculated with the time parametrization of Omicron. For Germany and Italy, staggered difference-in-differences analysis was employed to explore the causal relationship between school reopening and R c changes, accounting for varying reopening dates. In Portugal, interrupted time series analysis was used due to simultaneous school reopenings. Multivariable models were adopted to adjust for confounders. In Italy and Germany, post-reopening R c estimates were significantly lower compared to those from regions/states that had not yet reopened at the same time points, both in the student population (overall average treatment effect for the treated subpopulation [O-ATT]: -0.80 [95% CI: -0.94;-0.66] for Italy; O-ATT-0.30 [95% CI: -0.36;-0.23] for Germany) and the adult population (O-ATT: -0.04 [95% CI: -0.07;-0.01] for Italy; O-ATT: -0.07 [95% CI: -0.11;-0.03] for Germany). In Portugal, there was a significant decreasing trend in R c following school reopenings compared to the pre-reopening period (sustained effect: -0.03 [95% CI: -0.04; -0.03] in students; -0.02 [95% CI: -0.03; -0.02] in adults). We found no evidence of a causal relationship between school reopenings in autumn 2022 and Omicron SARS-CoV-2 transmission. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper delves into the persistent issue of unequal access to medical imaging, with a particular focus on breast cancer screening and its impact on marginalized communities and racial/ethnic minorities. Central to our discussion is the role of scientific mobility among radiologists in fostering healthcare policy changes that promote diversity and cultural competence. We propose various strategies to bridge this gap, including cultural education, sensitivity training, and diversifying the radiology workforce. These measures aim to improve communication with diverse patient groups and reduce healthcare disparities. Additionally, we explore the challenges and advantages of teleradiology as a means to extend medical imaging services to underserved areas. In the context of artificial intelligence, we emphasize the critical need to validate algorithms across diverse populations to ensure unbiased and equitable healthcare outcomes. Overall, this paper underscores the importance of international collaboration in addressing global access barriers, presenting it as a key to mitigating disparities in medical imaging access and contributing to the pursuit of equitable healthcare. Access to medical imaging is pivotal in healthcare, playing a crucial role in the prevention, diagnosis, and management of diseases. However, disparities persist in this scenario, disproportionately affecting marginalized communities, racial and ethnic minorities, and individuals facing linguistic or cultural barriers. This paper critically assesses methods to mitigate these disparities, with a focus on breast cancer screening. We underscore scientific mobility as a vital tool for radiologists to advocate for healthcare policy changes: it not only enhances diversity and cultural competence within the radiology community but also fosters international cooperation and knowledge exchange among healthcare institutions. Efforts to ensure cultural competency among radiologists are discussed, including ongoing cultural education, sensitivity training, and workforce diversification. These initiatives are key to improving patient communication and reducing healthcare disparities. This paper also highlights the crucial role of policy changes and legislation in promoting equal access to essential screening services like mammography. We explore the challenges and potential of teleradiology in improving access to medical imaging in remote and underserved areas. In the era of artificial intelligence, this paper emphasizes the necessity of validating its models across a spectrum of populations to prevent bias and achieve equitable healthcare outcomes. Finally, the importance of international collaboration is illustrated, showcasing its role in sharing insights and strategies to overcome global access barriers in medical imaging. Overall, this paper offers a comprehensive overview of the challenges related to disparities in medical imaging access and proposes actionable strategies to address these challenges, aiming for equitable healthcare delivery. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Prognostic Value of 25-hydroxyvitamin D3 Levels at Diagnosis and During Follow-up in Melanoma Patients."

Simple Summary: The introduction of targeted therapy has completely changed the treatment options for patients with advanced non-small cell lung cancer (NSCLC). Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers for lung cancer, are thus warranted to improve the management of advanced NSCLC, including adenocarcinoma, and move toward personalized therapy. Radiomics aims to extract high-dimensional features from clinical images in order to find any association with specific clinical endpoints. The aim of this study is to investigate the role of CT radiomics for non-invasive prediction of prognosis and identification of actionable genomic alterations in advanced lung adenocarcinoma patients. Findings from this study support a possible role of CT radiomics in the clinical management of patients with advanced lung cancer; moreover, its findings can contribute to design robust validation studies. Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status (EGFR, KRAS, ALK alterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95–0.98); validation performance was good for EGFR (AUC 0.86), moderate for KRAS and ALK (AUC 0.61–0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies. [ABSTRACT FROM AUTHOR]

Systemic amyloidosis is caused by misfolding and aggregation of globular proteins in vivo for which effective treatments are urgently needed. Inhibition of protein self-aggregation represents an attractive therapeutic strategy. Studies on the amyloidogenic variant of β2-microglobulin, D76N, causing hereditary systemic amyloidosis, have become particularly relevant since fibrils are formed in vitro in physiologically relevant conditions. Here we compare the potency of two previously described inhibitors of wild type β2-microglobulin fibrillogenesis, doxycycline and single domain antibodies (nanobodies). The β2-microglobulin -binding nanobody, Nb24, more potently inhibits D76N β2-microglobulin fibrillogenesis than doxycycline with complete abrogation of fibril formation. In β2-microglobulin knock out mice, the D76N β2-microglobulin/ Nb24 pre-formed complex, is cleared from the circulation at the same rate as the uncomplexed protein; however, the analysis of tissue distribution reveals that the interaction with the antibody reduces the concentration of the variant protein in the heart but does not modify the tissue distribution of wild type β2-microglobulin. These findings strongly support the potential therapeutic use of this antibody in the treatment of systemic amyloidosis. [ABSTRACT FROM AUTHOR]

Objectives: To determine if radiomic features, alone or combined with clinical data, are associated with residual tumour (RT) at surgery, and predict the risk of disease progression within 12 months (PD12) in ovarian cancer (OC) patients.Methods: This retrospective study enrolled 101 patients according to the following inclusion parameters: cytoreductive surgery performed at our institution (9 May 2007-23 February 2016), assessment of BRCA mutational status, preoperative CT available. Radiomic features of the ovarian masses were extracted from 3D structures drawn on CT images. A phantom experiment was performed to assess the reproducibility of radiomic features. The final radiomic features included in the analysis (n = 516) were grouped into clusters using a hierarchical clustering procedure. The association of each cluster's representative radiomic feature with RT and PD12 was assessed by chi-square test. Multivariate analysis was performed using logistic regression models. P values < 0.05 were considered significant.Results: Patients with values of F2-Shape/Compactness1 below the median, of F1- GrayLevelCooccurenceMatrix25/0-1InformationMeasureCorr2 below the median and of F1-GrayLevelCooccurenceMatrix25/-333-1InverseVariance above the median showed higher risk of RT (36%, 36% and 35%, respectively, as opposed to 18%, 18% and 18%). Patients with values of F4-GrayLevelRunLengthMatrix25/-333RunPercentage above the median, of F2 shape/Max3DDiameter below the median and F1-GrayLevelCooccurenceMatrix25/45-1InverseVariance above the median showed higher risk of PD12 (22%, 24% and 23%, respectively, as opposed to 6%, 5% and 6%). At multivariate analysis F2-Shape/Max3DDiameter remained significant (odds ratio (95% CI) = 11.86 (1.41-99.88)). To predict PD12, a clinical radiomics model performed better than a base clinical model.Conclusion: This study demonstrated significant associations between radiomic features and prognostic factors such as RT and PD12.Key Points: • No residual tumour (RT) at surgery is the most important prognostic factor in OC. • Radiomic features related to mass size, randomness and homogeneity were associated with RT. • Progression of disease within 12 months (PD12) indicates worse prognosis in OC. • A model including clinical and radiomic features performed better than only-clinical model to predict PD12. [ABSTRACT FROM AUTHOR]

We conducted a systematic review of studies that investigated whether quitting smoking at or around diagnosis improves survival of patients with hormone-dependent cancers (HDC). Nine studies published in 2013–2022 were included. Studies were very diverse in terms of design, definition of quitters and continued smokers, and prevalence of prognostic factors other than smoking cessation (e.g. patients' demographics, tumour characteristic, and treatments). For breast, ovarian, and endometrial cancer, all included studies found that quitters had better overall, disease specific, and disease-free survival than continued smokers. For prostate cancer, there was no evidence of an association of smoking cessation with improved survival. This literature review provided suggestive evidence that female smokers diagnosed with cancer of the breast, ovary, or endometrium may improve their chances of surviving by stopping smoking. Smoking cessation counselling should become part of standard oncological care for these patients and integrated into breast cancer screening programs. [Display omitted] • Does cessation improves survival of smokers with hormone-dependent cancers (HDC). • We conducted a literature review (up to April 2022) and found 9 eligible studies. • For female HDC, quitters generally have better survival than continued smokers. • No evidence of an association was suggested for men with prostate cancer. • Smoking cessation should be part of standard oncological care for active smokers. [ABSTRACT FROM AUTHOR]

Simple Summary: Oropharyngeal squamous cell carcinoma (OPSCC) has one of the most rapidly increasing incidences of any cancer in high-income countries. The aim of this study is to test whether radiomic and blood-derived biomarkers are good candidates for refining the prognostic stratification in OPSCC. The results show that the integration of clinical, immunological, and computed tomography-derived features generally yields an improvement, regardless of the HPV status, in the prognostic stratification of OPSCC patients who are candidates for curative-intent radiotherapy. Specifically, we documented a significant role of the Lymphocyte-to-Monocyte Ratio (LMR) in this population, which has been scarcely investigated in OPSCC, as well as the detrimental effects of lymphopenia and anemia. Results are promising, and model performances compare favorably with available radiomic scores in the same setting. Further investigations on our findings are warranted to validate the results and include a more in-depth study of the prognostic role of the LMR in OPSCC. Future analyses of this dataset are planned to provide a more complete overview of the tumor-immune system interplay. Aims: To assess whether CT-based radiomics and blood-derived biomarkers could improve the prediction of overall survival (OS) and locoregional progression-free survival (LRPFS) in patients with oropharyngeal cancer (OPC) treated with curative-intent RT. Methods: Consecutive OPC patients with primary tumors treated between 2005 and 2021 were included. Analyzed clinical variables included gender, age, smoking history, staging, subsite, HPV status, and blood parameters (baseline hemoglobin levels, neutrophils, monocytes, and platelets, and derived measurements). Radiomic features were extracted from the gross tumor volumes (GTVs) of the primary tumor using pyradiomics. Outcomes of interest were LRPFS and OS. Following feature selection, a radiomic score (RS) was calculated for each patient. Significant variables, along with age and gender, were included in multivariable analysis, and models were retained if statistically significant. The models' performance was compared by the C-index. Results: One hundred and five patients, predominately male (71%), were included in the analysis. The median age was 59 (IQR: 52–66) years, and stage IVA was the most represented (70%). HPV status was positive in 63 patients, negative in 7, and missing in 35 patients. The median OS follow-up was 6.3 (IQR: 5.5–7.9) years. A statistically significant association between low Hb levels and poorer LRPFS in the HPV-positive subgroup (p = 0.038) was identified. The calculation of the RS successfully stratified patients according to both OS (log-rank p < 0.0001) and LRPFS (log-rank p = 0.0002). The C-index of the clinical and radiomic model resulted in 0.82 [CI: 0.80–0.84] for OS and 0.77 [CI: 0.75–0.79] for LRPFS. Conclusions: Our results show that radiomics could provide clinically significant informative content in this scenario. The best performances were obtained by combining clinical and quantitative imaging variables, thus suggesting the potential of integrative modeling for outcome predictions in this setting of patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is the most frequent cancer among women: early diagnosis and management of breast lesions are crucial to achieve a better prognosis for patients with this diagnosis. Breast ultrasound (US) is one of the main techniques for the management of breast lesions and it is important in doubtful findings on mammography and in the evaluation of dense breasts. Unfortunately, US has a high rate of false positive and has high operator dependence. Ultrasound CAD (computer-aided diagnosis) and radiomics are newly developed tools that can help solve these issues: this study aims to create a radiomics score from breast US to predict malignancy of a breast lesion, and to also combine this score with CAD and sonographer performances. Finally, we would like to create a prediction tool of US radiomics features combined with CAD, clinical parameters, and Breast Imaging Reporting and Data System evaluation for the prediction of malignancy of breast lesions. The study aimed to evaluate the performance of radiomics features and one ultrasound CAD (computer-aided diagnosis) in the prediction of the malignancy of a breast lesion detected with ultrasound and to develop a nomogram incorporating radiomic score and available information on CAD performance, conventional Breast Imaging Reporting and Data System evaluation (BI-RADS), and clinical information. Data on 365 breast lesions referred for breast US with subsequent histologic analysis between January 2020 and March 2022 were retrospectively collected. Patients were randomly divided into a training group (n = 255) and a validation test group (n = 110). A radiomics score was generated from the US image. The CAD was performed in a subgroup of 209 cases. The radiomics score included seven radiomics features selected with the LASSO logistic regression model. The multivariable logistic model incorporating CAD performance, BI-RADS evaluation, clinical information, and radiomic score as covariates showed promising results in the prediction of the malignancy of breast lesions: Area under the receiver operating characteristic curve, [AUC]: 0.914; 95% Confidence Interval, [CI]: 0.876–0.951. A nomogram was developed based on these results for possible future applications in clinical practice. [ABSTRACT FROM AUTHOR]

Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models' accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings. [ABSTRACT FROM AUTHOR]

We aimed to investigate the association between the radiomic features of contrast-enhanced spectral mammography (CESM) images and a specific receptor pattern of breast neoplasms. In this single-center retrospective study, we selected patients with neoplastic breast lesions who underwent CESM before a biopsy and surgical assessment between January 2013 and February 2022. Radiomic analysis was performed on regions of interest selected from recombined CESM images. The association between the features and each evaluated endpoint (ER, PR, Ki-67, HER2+, triple negative, G2–G3 expressions) was investigated through univariate logistic regression. Among the significant and highly correlated radiomic features, we selected only the one most associated with the endpoint. From a group of 321 patients, we enrolled 205 malignant breast lesions. The median age at the exam was 50 years (interquartile range (IQR) 45–58). NGLDM_Contrast was the only feature that was positively associated with both ER and PR expression (p-values = 0.01). NGLDM_Coarseness was negatively associated with Ki-67 expression (p-value = 0.02). Five features SHAPE Volume(mL), SHAPE_Volume(vx), GLRLM_RLNU, NGLDM_Busyness and GLZLM_GLNU were all positively and significantly associated with HER2+; however, all of them were highly correlated. Radiomic features of CESM images could be helpful to predict particular molecular subtypes before a biopsy. [ABSTRACT FROM AUTHOR]

Cigarette smoking is the main risk factor for head and neck cancer (HNC) and many HNC patients are active smokers at diagnosis. We conducted a systematic literature review and meta-analysis to quantify the survival impact of smoking cessation at or around the time of HNC diagnosis. We searched studies published until December 31, 2021, and used random-effects meta-analysis to pool study-specific estimates into summary hazard ratio (SHR) and corresponding 95% confidence intervals (CI). Sixteen studies were published between 1983 and 2021, and over 2300 HNC patients were included. Studies were diverse in terms of design, patients, tumours and treatment characteristics, and criteria used to discriminate quitters from continued smokers. HNC patients who quit smoking at or around diagnosis had significantly better overall survival than continued smokers (SHR 0.80, 95% CI 0.70-0.91, n studies = 10). A beneficial effect of post-diagnosis smoking cessation was suggested for other survival endpoints as well, but the results were based on fewer studies (n = 5) and affected by publication bias. Cessation counselling should be offered to all smokers who start a diagnostic workup for HNC and should be considered standard multidisciplinary oncological care for HNC patients. PROSPERO registration number CRD42021245560. [ABSTRACT FROM AUTHOR]

It was suggested that vitamin D levels influence cancer development. The vitamin D receptor (VDR) is a crucial mediator for the cellular effects of vitamin D. Results from previous studies on the association of VDR polymorphisms with different cancer types are somewhat contradictory, and the role of VDR in the etiology of cancer is still equivocal. We therefore performed a meta-analysis on the association between the two most studied VDR polymorphisms (FokI and BsmI) and any cancer site. Up to January 2009, we identified 67 independent studies. We used random-effects models to provide summary odds ratio (SOR) for VDR polymorphisms and cancer. We tested homogeneity of effects across studies and publication bias and explored between-study heterogeneity. When comparing FokI ff with FF carriers, we found a significant increase in skin cancer [SOR; 95% confidence intervals (CIs): 1.30; 1.04–1.61] and breast cancer (SOR; 95%CI: 1.14; 1.03–1.27) risk. For the same genotype comparison, we found a significantly higher risk of cancer when we pooled estimates from cancer sites possibly associated with vitamin D levels (prostate, breast, skin, ovary, non-Hodgkin lymphoma and colorectal). A significant reduction in prostate cancer risk was observed for carriers of BsmI Bb compared with bb genotype (SOR; 95%CI: 0.83; 0.69–0.99). In Caucasian populations, both Bb and BB carriers had a significant reduced risk of cancer at any site. In conclusion, this meta-analysis showed that VDR FokI and BsmI polymorphisms might modulate the risk of cancer of breast, skin and prostate and possibly affect cancer risk at any site in Caucasians. [ABSTRACT FROM AUTHOR]

Objective: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model's use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours. Methods: The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions. Results: Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time. Conclusion: We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior. [ABSTRACT FROM AUTHOR]

Simple Summary: Smokers are at high risk of cancer of the gastrointestinal system, and many patients with newly diagnosed cancer of the oesophagus, stomach, colon-rectum, and liver are active smokers at diagnosis. In this review, we focused on whether stopping smoking shortly before diagnosis or afterwards (e.g., during treatment) may improve the chance of survival for these patients. We reviewed the scientific literature up to April 2022 and found only seven articles focusing on this topic. While very limited in number, these studies provided suggestive evidence in favour of a beneficial effect of smoking cessation for these patients. Smokers with newly diagnosed cancer of the gastrointestinal system should be encouraged to stop smoking and provided all the necessary support to achieve this goal. Cigarette smoking is a strong risk factor for the occurrence of gastrointestinal cancers, and a substantial proportion of newly diagnosed patients is made up of active smokers, yet the impact of smoking cessation at or around diagnosis on the clinical course of these cancers (whose prognosis is often unfavourable) has never been summarized to date. We reviewed studies published until 30 April 2022 that investigated whether smoking cessation at or around diagnosis favourably affects the clinical course of gastrointestinal cancers patients. Six studies were included for colorectal cancer patients, which provided limited yet suggestive evidence that quitters may have longer disease-specific survival compared to continued smokers. Only one study each focused on patients with gastric or HBV-positive liver cancer (both reporting a survival advantage for quitters vs. continued smokers), while we found no eligible studies for patients with cancer at other sites within the digestive system. More research is urgently needed to expand the evidence on the topic, given the potentially major clinical implications for these patients. Moreover, health professionals should provide the necessary smoking cessation support to any smoker who is undergoing diagnostic work-up or treatment for gastrointestinal cancer. [ABSTRACT FROM AUTHOR]

To assess the evidence on SARS-CoV2 infection and Covid-19 in relation to deficiency and supplementation of vitamin D, we conducted a systematic review up to April 2021. We summarised data from 38 eligible studies, which presented risk estimates for at least one endpoint, including two RCT and 27 cohort-studies: 205565 patients with information on 25OHD status and 2022 taking vitamin D supplementation with a total of 1197 admitted to the ICU or who needed invasive mechanical ventilation or intubation and hospital stay, and more than 910 Covid-19 deaths. Primary outcomes were severity and mortality and the main aim was to evaluate the association with vitamin D supplementation. Random effects models showed that supplementation was associated with a significant lower risk of both Covid-19 severe disease (SRR 0.38, 95% CI 0.20–0.72, 6 studies) and mortality (SRR 0.35, 95% CI 0.17–0.70, 8 studies). There were no statistically significant dose differences between studies: summary estimates with regular doses remain statistically significant, suggesting that higher doses are not necessary. For patients on vitamin D supplementation, a greater reduction in mortality risk emerged in older individuals and at higher latitudes. Regarding the quality of studies, assessed using the New Castle-Ottawa quality scale, the analysis revealed in most cases no statistically significant differences between low, medium or high quality studies. We found significant associations of vitamin D supplementation with Covid-19, encompassing risks of disease worsening and mortality, especially in seasons characterized by 25OHD deficiency and with not severe patients. Dedicated randomized clinical studies are encouraged to confirm these results. [ABSTRACT FROM AUTHOR]

Simple Summary: Ovarian cancer has a heterogeneous response to treatment, and relapse may vary considerably. Different studies investigated the role of radiomics in ovarian cancer. However, many of them were performed in a single center, and solid external validation of findings is still missing. We used a multicentric database of high-grade serous ovarian cancer to build predictive radiomic and deep-learning models for early relapse and BRCA mutation, validating them in a different set of cases coming from other institutions. In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status with both traditional radiomics and deep learning methods. This study highlights that to implement the radiomics approach in clinical routine, we still need standardization of acquisition protocols, validation of harmonization method and radiomic pipelines, other than robust, prospective, multicentric, external validations of findings. Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. Methods: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. Results: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). Conclusions: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results. [ABSTRACT FROM AUTHOR]

Objective: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.Methods: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.Results: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.Conclusions: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.Clinical relevance statement: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.Key Points: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow. [ABSTRACT FROM AUTHOR]