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The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal−Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68−0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.

Background: To evaluate the diagnostic performance of PSMA-PET compared to conventional imaging/liver biopsy in the detection of liver metastases in CRPC patients. Moreover, we evaluated a PSMA-PET/CT-based radiomic model able to identify liver metastases. Methods: Multicenter retrospective study enrolling patients with the following inclusion criteria: (a) proven CRPC patients, (b) PSMA-PET and conventional imaging/liver biopsy performed in a 6 months timeframe, (c) no therapy changes between PSMA-PET and conventional imaging/liver biopsy. PSMA-PET sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for liver metastases were calculated. After the extraction of radiomic features, a prediction model for liver metastases identification was developed. Results: Sixty CRPC patients were enrolled. Within 6 months before or after PSMA-PET, conventional imaging and liver biopsy identified 24/60 (40%) patients with liver metastases. PSMA-PET sensitivity, specificity, PPV, NPV, and accuracy for liver metastases were 0.58, 0.92, 0.82, 0.77, and 0.78, respectively. Either number of liver metastases and the maximum lesion diameter were significantly associated with the presence of a positive PSMA-PET (p < 0.05). On multivariate regression analysis, the radiomic feature-based model combining sphericity, and the moment of inverse difference (Idm), had an AUC of 0.807 (95% CI:0.686-0.920). Conclusion: For liver metastases assessment, [68Ga]Ga-PSMA-11-PET demonstrated moderate sensitivity while high specificity, PPV, and inter-reader agreement compared to conventional imaging/liver biopsy in CRPC patients.

Background: We investigated the diagnostic accuracy of the new Prostate Imaging for Recurrence Reporting (PI-RR) score and its inter-observer variability. Secondly, we compared the detection rate of PI-RR and PET and analyzed the correlation between Prostate Specific Antigen (PSA) levels and the PI-RR score. Methods: We included in the analysis 134 patients submitted to multiparametric magnetic resonance imaging for suspected local recurrence. The images were independently reviewed by two radiologists, assigning a value from 1 to 5 to the PI-RR score. Inter-observer agreement and diagnostic accuracy of the PI-RR score (compared to histopathological data, available for 19 patients) were calculated. The detection rate was compared to those of choline PET/CT (46 patients) and PSMA PET/CT (22 patients). The distribution of the PSA values in relation to the PI-RR scores was also analyzed. Results: The accuracy of the PI-RR score was 68.4%. The reporting agreement was excellent (K = 0.884, p < 0.001). The PI-RR showed a higher detection rate than choline PET/CT (69.6% versus 19.6%) and PSMA PET-CT (59.1% versus 22.7%). The analysis of the PSA distribution documented an increase in the PI-RR score as the PSA value increased. Conclusion: The excellent reproducibility of the PI-RR score supports its wide use in the clinical practice to standardize recurrence reporting. The detection rate of PI-RR was superior to that of PET, but was linked to the PSA level.

Background: Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using a fully automated artificial intelligence-based model for the segmentation and consequent characterization of the tumor areas using radiomic features was evaluated. The TRG score was used to evaluate the clinical outcome., Methods: Forty-three patients under treatment in the IRCCS Sant'Orsola-Malpighi Polyclinic were retrospectively selected for the study; a U-Net model was trained for the automated segmentation of the tumor areas; the radiomic features were collected and used to predict the tumor regression grade (TRG) score., Results: The segmentation of tumor areas outperformed the state-of-the-art results in terms of the Dice score coefficient or was comparable to them but with the advantage of considering mucinous cases. Analysis of the radiomic features extracted from the lesion areas allowed us to predict the TRG score, with the results agreeing with the state-of-the-art results., Conclusions: The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice.

The burden of post-operative complications of patients undergoing liver resection for hepatocellular carcinoma (HCC) is a cause of morbidity and mortality. Recently, sarcopenia has been reported to influence the outcome of patients with cirrhosis. We aimed to assess factors associated with sarcopenia and its prognostic role in liver surgery candidates. We included all patients with compensated advanced chronic liver disease (cACLD) undergoing liver resection for primary HCC consecutively referred to the University of Bologna from 2014 to 2019 with an available preoperative abdominal CT-scan performed within the previous three months. A total of 159 patients were included. The median age was 68 years, and 80.5% of the patients were male. Sarcopenia was present in 82 patients (51.6%). Age and body mass index (BMI) were associated with the presence of sarcopenia at multivariate analysis. Thirteen (8.2%) patients developed major complications and 14 (8.9%) presented PHLF grade B-C. The model for end-stage liver disease score was associated with the development of major complications, whereas cACLD presence, thrombocytopenia, portal hypertension (PH), Child-Pugh score and Albumin-Bilirubin score were found to be predictors of clinically significative PHLF. The rate of major complications was 11.8% in sarcopenic patients with cACLD compared with no complications (0%) in patients without sarcopenia and cACLD ( p = 0.032). The rate of major complications was significantly higher in patients with (16.3%) vs. patients without (0%) sarcopenia ( p = 0.012) in patients with PH. In conclusion, sarcopenia, which is associated with age and BMI, may improve the risk stratification of post-hepatectomy major complications in patients with cACLD and PH.

Background: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist's tumour segmentation., Methods: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics., Results: The original 89 HCC nodules (32 MVI+ and 57 MVI-) became 169 (62 MVI+ and 107 MVI-) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI-), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70-0.93), p ∼10-5), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively., Conclusions: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status.

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging-Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.

Treatment response is usually assessed by the response evaluation criteria in solid tumors (RECIST). These criteria may not be adequate to evaluate the response to immunotherapy, considering the peculiar patterns of response reported with this therapy. With the advent of immunotherapy these criteria have been modified to include the evaluation of the peculiar responses seen with this type of therapy (iRECIST criteria), including pseudoprogressions and hyperprogressions. Tumor growth rate (TGR) is a dynamic evaluation that takes into account the kinetics of response to treatment and may help catch the real efficacy of an immunotherapy approach. We performed a retrospective monocentric study to explore the impact of TGR change after nivolumab administration as the second or later line of treatment in patients with metastatic renal cell carcinoma (RCC). We evaluated 27 patients, divided into three categories: Disease control (DC) if there was no PD; lower velocity PD (LvPD) if disease progressed but the TGR at second assessment (TGR2) was lower than the TGR at first assessment (TGR1); higher velocity PD (HvPD) if TGR2 was higher than TGR1. The median OS for the DC group was 11.0 months (95% CI 5.0-17.0) (reference) vs. (not reached) NR (95% CI NR-NR) for LvPD (HR 0.27; 95% CI 0.06-1.30; p 0.102) vs. NR (95% CI NR-NR) for HvPD (HR 0.23; 95% CI 0.06-0.88; p 0.032). There was no difference between LvPD and DC (HR 1.21; 95% CI 0.20-7.28; p 0.838). In patients with metastatic RCC, the second or later line of nivolumab treatment may lead to a deceleration in TGR resulting in an improved survival outcome similar to that observed in patients experiencing tumor regression. In this subgroup, especially in the presence of a clinical benefit, continuing the treatment beyond progression can be recommended.

Cholangiocarcinoma (CCA) is a primary and aggressive cancer of the biliary tree. Combined hepatocellular cholangiocarcinoma (CHC) is a distinctive primary liver malignancy which has properties of both hepatocytic and cholangiocytic differentiation. CHC appears to have a worse prognosis compared to hepatocellular carcinoma, and similar to that of intrahepatic CCA. While significant advances have been made in understanding the pathophysiology and treatment of these two tumor types, their prognosis remains poor. Currently, liver resection is the primary treatment modality; however, only a minority of patients are eligible for surgery. However, the use of locoregional therapies proves an alternative approach to treating locally advanced disease with the aim of converting to resectability or even transplantation. Locoregional therapies such as transarterial chemoembolization (TACE), selective internal radiation therapy (SIRT), radiofrequency ablation (RFA), and photodynamic therapy (PDT) can provide patients with tumor control and increase the chances of survival. In this review, we appraise the evidence surrounding the use of locoregional therapies in treating patients with CCA and CHC.

The study aimed to generate a local failure (LF) risk map in resected pancreatic cancer (PC) and validate the results of previous studies, proposing new guidelines for PC postoperative radiotherapy clinical target volume (CTV) delineation. Follow-up computer tomography (CT) of resected PC was retrospectively reviewed by two radiologists identifying LFs and plotting them on a representative patient CT scan. The percentages of LF points randomly extracted based on CTV following the RTOG guidelines and based on the LF database were 70% and 30%, respectively. According to the Kernel density estimation, an LF 3D distribution map was generated and compared with the results of previous studies using a Dice index. Among the 64 resected patients, 59.4% underwent adjuvant treatment. LFs closer to the root of the celiac axis (CA) or the superior mesenteric artery (SMA) were reported in 32.8% and 67.2% cases, respectively. The mean (± standard deviation) distances of LF points to CA and SMA were 21.5 ± 17.9 mm and 21.6 ± 12.1 mm, respectively. The Dice values comparing our iso-level risk maps corresponding to 80% and 90% of the LF probabilistic density and the CTVs-80 and CTVs-90 of previous publications were 0.45-0.53 and 0.58-0.60, respectively. According to the Kernel density approach, a validated LF map was proposed, modeling a new adjuvant CTV based on a PC pattern of failure.

Background: Structured reporting (SR) in oncologic imaging is becoming necessary and has recently been recognized by major scientific societies. The aim of this study was to build MRI-based structured reports for rectal cancer (RC) staging and restaging in order to provide clinicians all critical tumor information., Materials and Methods: A panel of radiologist experts in abdominal imaging, called the members of the Italian Society of Medical and Interventional Radiology, was established. The modified Delphi process was used to build the SR and to assess the level of agreement in all sections. The Cronbach's alpha (Cα) correlation coefficient was used to assess the internal consistency of each section and to measure the quality analysis according to the average inter-item correlation. The intraclass correlation coefficient (ICC) was also evaluated., Results: After the second Delphi round of the SR RC staging, the panelists' single scores and sum of scores were 3.8 (range 2-4) and 169, and the SR RC restaging panelists' single scores and sum of scores were 3.7 (range 2-4) and 148, respectively. The Cα correlation coefficient was 0.79 for SR staging and 0.81 for SR restaging. The ICCs for the SR RC staging and restaging were 0.78 ( p < 0.01) and 0.82 ( p < 0.01), respectively. The final SR version was built and included 53 items for RC staging and 50 items for RC restaging., Conclusions: The final version of the structured reports of MRI-based RC staging and restaging should be a helpful and promising tool for clinicians in managing cancer patients properly. Structured reports collect all Patient Clinical Data, Clinical Evaluations and relevant key findings of Rectal Cancer, both in staging and restaging, and can facilitate clinical decision-making.

Gastric cancer (GC) is a common cancer worldwide. Its incidence and mortality vary depending on geographic area, with the highest rates in Asian countries, particularly in China, Japan, and South Korea. Accurate imaging staging has become crucial for the application of various treatment strategies, especially for curative treatments in early stages. Unfortunately, most GCs are still diagnosed at an advanced stage, with the peritoneum (61-80%), distant lymph nodes (44-50%), and liver (26-38%) as the most common metastatic locations. Metastatic disease is limited to the peritoneum in 58% of cases; in nonperitoneal distant metastases, the most involved GC metastasization site is the liver (82%). The eighth edition of the tumor-node-metastasis staging system is the most commonly used system for determining GC prognosis. Endoscopic ultrasonography, computed tomography, and 18-fluorideoxyglucose positron emission tomography are historically the most accurate imaging techniques for GC staging. However, studies have recently shown renewed interest in magnetic resonance imaging (MRI) as a useful tool in GC staging, especially for distant metastasis assessment. The technical improvement of diffusion-weighted imaging and the increasing use of hepatobiliary contrast agents have been shown to increase the diagnostic performance of MRI, particularly for detecting peritoneal and liver metastasis. However, no principal oncological guidelines have included the use of MRI as a first-line technique for distant metastasis evaluation during the GC staging process, such as the National Comprehensive Cancer Network Guidelines. This review analyzed the role of the principal imaging techniques in GC diagnosis and staging, focusing on the potential role of MRI, especially for assessing peritoneal and liver metastases.

Combined hepatocellular and cholangiocarcinoma (HCC-CC) is a rare primary liver cancer. It is constituted by neoplastic cells of both hepatocellular and cholangiocellular derivation. Different histology types of HCC-CC have been reported, hinting at heterogeneous carcinogenic pathways leading to the development of this cancer. Due to its rarity and complexity, mixed HCC-CC is a scantly investigated condition with unmet needs and unsatisfactory outcomes. Surgery remains the preferred treatment in resectable patients. The risk of recurrence, however, is high, especially in comparison with other primary liver cancers such as hepatocellular carcinoma. In unresectable or recurring patients, the therapeutic options are challenging due to the dual nature of the neoplastic cells. Consequently, the odds of survival of patients with HCC-CC remains poor. We analysed the literature systematically about the treatment of mixed HCC-CC, reviewing the main therapeutic options and their outcomes and analysing the most interesting developments in this topic with a focus on new potential therapeutic avenues.

Computed tomography (CT), magnetic resonance imaging (MRI), and 18-fluorideoxyglucose positron emission tomography ( 18 FDG-PET) are historically the most accurate imaging techniques for diagnosing liver metastases. Recently, the combination of diffusion-weighted imaging and hepatospecific contrast media, such as gadoxetic acid in MRI, have been demonstrated to have the highest diagnostic accuracy, sensitivity, and specificity for detecting liver metastases. Various recent meta-analyses have confirmed the diagnostic superiority of this combination (diffusion-weighted imaging and gadoxetic acid-enhanced MRI), especially in terms of per lesion sensitivity, as compared with CT and 18 FDG-PET, even for smaller lesions (≤1 cm). However, none of the oncological guidelines have suggested the use of MRI as a first-line technique for liver metastasis detection during the staging process of oncological patients. This review analyzes the history of the principal imaging techniques for the diagnosis of liver metastases, in particular of colorectal liver metastases, focusing on the most accurate method (diffusion-weighted imaging combined with gadoxetic acid-enhanced MRI), possible reasons for the lack of its diffusion in the guidelines, and possible future scenarios., Competing Interests: The authors declare no conflict of interest.