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Simple Summary: For patients with colorectal cancer with liver metastases, it is important to determine the genetic mutations (e.g., KRAS mutations) of the liver metastases. Around 35–45% of patients with colorectal cancer liver metastases (CRLM) have a KRAS mutation, and genetic mutations are used in treatment planning and prognostication. The aim of this study was to assess if KRAS mutations could be identified on CT scans using radiomics. In the discovery cohort of 255 patients, KRAS mutations could be identified with a good accuracy. In the external validation cohort consisting of 129 patients, the radiomics model performed poorly. These results indicate that radiomics might be used to determine genetic mutations such as KRAS, but foremost emphasize the importance of the external validation of radiomics models. External validation is crucial for the assessment of clinical applicability and should be mandatory in all future studies in the field of radiomics. For patients with colorectal cancer liver metastases (CRLM), the genetic mutation status is important in treatment selection and prognostication for survival outcomes. This study aims to investigate the relationship between radiomics imaging features and the genetic mutation status (KRAS mutation versus no mutation) in a large multicenter dataset of patients with CRLM and validate these findings in an external dataset. Patients with initially unresectable CRLM treated with systemic therapy of the randomized controlled CAIRO5 trial (NCT02162563) were included. All CRLM were semi-automatically segmented in pre-treatment CT scans and radiomics features were calculated from these segmentations. Additionally, data from the Netherlands Cancer Institute (NKI) were used for external validation. A total of 255 patients from the CAIRO5 trial were included. Random Forest, Gradient Boosting, Gradient Boosting + LightGBM, and Ensemble machine-learning classifiers showed AUC scores of 0.77 (95%CI 0.62–0.92), 0.77 (95%CI 0.64–0.90), 0.72 (95%CI 0.57–0.87), and 0.86 (95%CI 0.76–0.95) in the internal test set. Validation of the models on the external dataset with 129 patients resulted in AUC scores of 0.47–0.56. Machine-learning models incorporating CT imaging features could identify the genetic mutation status in patients with CRLM with a good accuracy in the internal test set. However, in the external validation set, the models performed poorly. External validation of machine-learning models is crucial for the assessment of clinical applicability and should be mandatory in all future studies in the field of radiomics. [ABSTRACT FROM AUTHOR]

Simple Summary: This systematic review evaluates the potential of magnetic resonance imaging (MRI) to predict tumor biology in primary squamous cell carcinoma of the head and neck (HNSCC). Fifty-eight articles were analyzed, examining the relationship between MRI parameters and biological features. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower diffusion-weighted metrics. Moreover, lower diffusion values were also with a high Ki-67 proliferation index, indicating high cellularity. Several perfusion parameters describing the vascularity were significantly associated with HIF-1α. Analysis results of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) were inconclusive. Larger datasets are needed to develop and validate radiomic-based prediction models, which already show promising results in capturing diverse tumor biology features. Overall, MRI holds potential for non-invasive and rapid tumor biology characterization, enhancing future clinical outcome predictions and personalized patient management for HNSCC. Magnetic resonance imaging (MRI) is an indispensable, routine technique that provides morphological and functional imaging sequences. MRI can potentially capture tumor biology and allow for longitudinal evaluation of head and neck squamous cell carcinoma (HNSCC). This systematic review and meta-analysis evaluates the ability of MRI to predict tumor biology in primary HNSCC. Studies were screened, selected, and assessed for quality using appropriate tools according to the PRISMA criteria. Fifty-eight articles were analyzed, examining the relationship between (functional) MRI parameters and biological features and genetics. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower ADCmean (SMD: 0.82; p < 0.001) and ADCminimum (SMD: 0.56; p < 0.001) values. On average, lower ADCmean values are associated with high Ki-67 levels, linking this diffusion restriction to high cellularity. Several perfusion parameters of the vascular compartment were significantly associated with HIF-1α. Analysis of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) yielded inconclusive results. Larger datasets with homogenous acquisition are required to develop and test radiomic-based prediction models capable of capturing different aspects of the underlying tumor biology. Overall, our study shows that rapid and non-invasive characterization of tumor biology via MRI is feasible and could enhance clinical outcome predictions and personalized patient management for HNSCC. [ABSTRACT FROM AUTHOR]

Background: Immune checkpoint inhibitor efficacy in advanced cancer patients remains difficult to predict. Imaging is the only technique available that can non-invasively provide whole body information of a patient's response to treatment. We hypothesize that quantitative whole-body prognostic information can be extracted by leveraging artificial intelligence (AI) for treatment monitoring, superior and complementary to the current response evaluation methods. Methods: To test this, a cohort of 74 stage-IV urothelial cancer patients (37 in the discovery set, 37 in the independent test, 1087 CTs), who received anti-PD1 or anti-PDL1 were retrospectively collected. We designed an AI system [named prognostic AI-monitor (PAM)] able to identify morphological changes in chest and abdominal CT scans acquired during follow-up, and link them to survival. Results: Our findings showed significant performance of PAM in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.73 (p < 0.001) for abdominal imaging, and 0.67 AUC (p < 0.001) for chest imaging. Subanalysis revealed higher accuracy of abdominal imaging around and in the first 6 months of treatment, reaching an AUC of 0.82 (p < 0.001). Similar accuracy was found by chest imaging, 5–11 months after start of treatment. Univariate comparison with current monitoring methods (laboratory results and radiological assessments) revealed higher or similar prognostic performance. In multivariate analysis, PAM remained significant against all other methods (p < 0.001), suggesting its complementary value in current clinical settings. Conclusions: Our study demonstrates that a comprehensive AI-based method such as PAM, can provide prognostic information in advanced urothelial cancer patients receiving immunotherapy, leveraging morphological changes not only in tumor lesions, but also tumor spread, and side-effects. Further investigations should focus beyond anatomical imaging. Prospective studies are warranted to test and validate our findings. [ABSTRACT FROM AUTHOR]

Background: Checkpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria. Methods: A cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients' follow-ups. A classifier was employed to link imaging features learned by the network with overall survival. Results: Our results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations. Conclusions: Our results demonstrate that deep learning can quantify tumor- and non–tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging. [ABSTRACT FROM AUTHOR]

Background: Road traffic injuries (RTIs) are a major public health concern in Libya. In the light of the armed conflict in Libya that broke out on February 2011 and the subsequent instability, the rate and pattern of RTIs was studied. Methods: RTI patient data were gathered from Al-Jalaa hospital, the main trauma center in Benghazi, from 2010 to 2011. Various parameters [i.e. age, gender, nationality, method of entry, receiving department, intensive care unit (ICU) admission, duration of stay, method of discharge, and fatalities] were compared with data from the previous year (2010), and statistical analyses were performed (t-test, chi-square, and Poisson regression). Results: During the conflict period, 15.8% (n=2,221) of hospital admissions were RTIs, that is, a rate of 6.08 RTI cases per day, levels not seen for 5 years (t=-5.719, p<0.001). The presence of armed conflict was found to have caused a significant 28% decrease in the trend of RTIs over the previous 10 years (B=-0.327, CI=-0.38 - -0.28, p<0.001). February and March, the peak period of active combat in Benghazi, witnessed the lowest number of RTIs during the conflict period. The average age of an RTI decreased to 28.35±16.3 years (t=-7.257, p<0.001) with significantly more males (84.1%, n=1,755) being affected (χ2=4.595, p=0.032, df=1). There was an increase in the proportion of younger aged patients (from 0 to 29 years) (χ2=29.874, p<0.001, df=8). More patients required admission to the ICU (χ2=36.808, p<0.001, df=8), and the mortality of an RTI increased to 5.2% (n=116) (χ2=48.882, p<0.001, df=6). Conclusions: There were fewer RTIs during the conflict period; however, those that occurred had higher morbidity and mortality. The profile of an RTI victims also changed to an increased prominence of young males and motorcyclists. Further research is required to propose and analyze possible interventions. [ABSTRACT FROM AUTHOR]

This paper aims to observe and to study the trends of road traffic accidents (RTA's) for the past ten years in Benghazi - Libya. A retrospective analysis was done using the patient records of Al-Jalaa hospital (the main trauma center in Benghazi) from over 21,753 RTA cases. The annual data were compared to each other and changes of trends were observed. RTA's represented an increasing percentage of Al-Jalaa's case load across the years. Around 41% of these cases needed to undergo surgery. The younger age group (20-29 years of age) formed the majority of cases while there was a trend towards an increasing average age of patients involved in an accident. Male patients were found to be younger than their female counterparts. Males comprised 81.5% while females formed 18.5% of RTA patients. In terms of inpatient duration, most patients stayed in the hospital for less than 1 week. Vehicle occupants (drivers and passengers) were admitted more often than pedestrians. There was a trend across the years towards an increased involvement of vehicle occupants and decrease in the proportion of pedestrians that had to be hospitalized. Additionally, there was a decrease in the fatalities of pedestrians. Overall, most RTA patients were discharged and made to follow-up in outpatient clinics however there was a startling trend towards increased LAMA and absconded patients. There were both encouraging findings as well as points that needed further emphasis and action. Public education, life support training and diversification of transport (apart from the use of the roads) should be looked into, as possible means of improving the current situation. [ABSTRACT FROM AUTHOR]

Purpose: In this systematic review and individual patient data (IPD) meta-analysis, we analysed the diagnostic performance of [18F]FDG PET/CT in detecting primary tumours in patients with CUP and evaluated whether the location of the predominant metastatic site influences the diagnostic performance.A systematic literature search from January 2005 to February 2024 was performed to identify articles describing the diagnostic performance of [18F]FDG PET/CT for primary tumour detection in CUP. Individual patient data retrieved from original articles or obtained from corresponding authors were grouped by the predominant metastatic site. The diagnostic performance of [18F]FDG PET/CT in detecting the underlying primary tumour was compared between predominant metastatic sites.A total of 1865 patients from 32 studies were included. The largest subgroup included patients with predominant bone metastases (n = 622), followed by liver (n = 369), lymph node (n = 358), brain (n = 316), peritoneal (n = 70), lung (n = 67), and soft tissue (n = 23) metastases, leaving a small group of other/undefined metastases (n = 40). [18F]FDG PET/CT resulted in pooled detection rates to identify the primary tumour of 0.74 (for patients with predominant brain metastases), 0.54 (liver-predominant), 0.49 (bone-predominant), 0.46 (lung-predominant), 0.38 (peritoneal-predominant), 0.37 (lymph node-predominant), and 0.35 (soft-tissue-predominant).This individual patient data meta-analysis suggests that the ability of [18F]FDG PET/CT to identify the primary tumour in CUP depends on the distribution of metastatic sites. This finding emphasises the need for more tailored diagnostic approaches in different patient populations. In addition, alternative diagnostic tools, such as new PET tracers or whole-body (PET/)MRI, should be investigated.Methods: In this systematic review and individual patient data (IPD) meta-analysis, we analysed the diagnostic performance of [18F]FDG PET/CT in detecting primary tumours in patients with CUP and evaluated whether the location of the predominant metastatic site influences the diagnostic performance.A systematic literature search from January 2005 to February 2024 was performed to identify articles describing the diagnostic performance of [18F]FDG PET/CT for primary tumour detection in CUP. Individual patient data retrieved from original articles or obtained from corresponding authors were grouped by the predominant metastatic site. The diagnostic performance of [18F]FDG PET/CT in detecting the underlying primary tumour was compared between predominant metastatic sites.A total of 1865 patients from 32 studies were included. The largest subgroup included patients with predominant bone metastases (n = 622), followed by liver (n = 369), lymph node (n = 358), brain (n = 316), peritoneal (n = 70), lung (n = 67), and soft tissue (n = 23) metastases, leaving a small group of other/undefined metastases (n = 40). [18F]FDG PET/CT resulted in pooled detection rates to identify the primary tumour of 0.74 (for patients with predominant brain metastases), 0.54 (liver-predominant), 0.49 (bone-predominant), 0.46 (lung-predominant), 0.38 (peritoneal-predominant), 0.37 (lymph node-predominant), and 0.35 (soft-tissue-predominant).This individual patient data meta-analysis suggests that the ability of [18F]FDG PET/CT to identify the primary tumour in CUP depends on the distribution of metastatic sites. This finding emphasises the need for more tailored diagnostic approaches in different patient populations. In addition, alternative diagnostic tools, such as new PET tracers or whole-body (PET/)MRI, should be investigated.Results: In this systematic review and individual patient data (IPD) meta-analysis, we analysed the diagnostic performance of [18F]FDG PET/CT in detecting primary tumours in patients with CUP and evaluated whether the location of the predominant metastatic site influences the diagnostic performance.A systematic literature search from January 2005 to February 2024 was performed to identify articles describing the diagnostic performance of [18F]FDG PET/CT for primary tumour detection in CUP. Individual patient data retrieved from original articles or obtained from corresponding authors were grouped by the predominant metastatic site. The diagnostic performance of [18F]FDG PET/CT in detecting the underlying primary tumour was compared between predominant metastatic sites.A total of 1865 patients from 32 studies were included. The largest subgroup included patients with predominant bone metastases (n = 622), followed by liver (n = 369), lymph node (n = 358), brain (n = 316), peritoneal (n = 70), lung (n = 67), and soft tissue (n = 23) metastases, leaving a small group of other/undefined metastases (n = 40). [18F]FDG PET/CT resulted in pooled detection rates to identify the primary tumour of 0.74 (for patients with predominant brain metastases), 0.54 (liver-predominant), 0.49 (bone-predominant), 0.46 (lung-predominant), 0.38 (peritoneal-predominant), 0.37 (lymph node-predominant), and 0.35 (soft-tissue-predominant).This individual patient data meta-analysis suggests that the ability of [18F]FDG PET/CT to identify the primary tumour in CUP depends on the distribution of metastatic sites. This finding emphasises the need for more tailored diagnostic approaches in different patient populations. In addition, alternative diagnostic tools, such as new PET tracers or whole-body (PET/)MRI, should be investigated.Conclusion: In this systematic review and individual patient data (IPD) meta-analysis, we analysed the diagnostic performance of [18F]FDG PET/CT in detecting primary tumours in patients with CUP and evaluated whether the location of the predominant metastatic site influences the diagnostic performance.A systematic literature search from January 2005 to February 2024 was performed to identify articles describing the diagnostic performance of [18F]FDG PET/CT for primary tumour detection in CUP. Individual patient data retrieved from original articles or obtained from corresponding authors were grouped by the predominant metastatic site. The diagnostic performance of [18F]FDG PET/CT in detecting the underlying primary tumour was compared between predominant metastatic sites.A total of 1865 patients from 32 studies were included. The largest subgroup included patients with predominant bone metastases (n = 622), followed by liver (n = 369), lymph node (n = 358), brain (n = 316), peritoneal (n = 70), lung (n = 67), and soft tissue (n = 23) metastases, leaving a small group of other/undefined metastases (n = 40). [18F]FDG PET/CT resulted in pooled detection rates to identify the primary tumour of 0.74 (for patients with predominant brain metastases), 0.54 (liver-predominant), 0.49 (bone-predominant), 0.46 (lung-predominant), 0.38 (peritoneal-predominant), 0.37 (lymph node-predominant), and 0.35 (soft-tissue-predominant).This individual patient data meta-analysis suggests that the ability of [18F]FDG PET/CT to identify the primary tumour in CUP depends on the distribution of metastatic sites. This finding emphasises the need for more tailored diagnostic approaches in different patient populations. In addition, alternative diagnostic tools, such as new PET tracers or whole-body (PET/)MRI, should be investigated. [ABSTRACT FROM AUTHOR]