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Background and Objectives : Sarcopenia, a condition characterized by muscle mass loss, is prevalent in up to 68% of rectal cancer patients and has been described as a negative prognostic factor, impacting overall survival and tumor response. While there are extensive data on rectal cancer globally, only a handful of studies have evaluated the role of sarcopenia in locally advanced rectal cancer (LARC). Our study aimed to investigate the relationship between sarcopenia, overall response rate, and toxicity in patients who underwent total neoadjuvant treatment (TNT) for LARC. Materials and Methods : We performed a retrospective study of patients with rectal cancer treated with TNT and surgery with curative intent between 2021 and 2023 at Prof. Dr. Ion Chiricuta Institute of Oncology, Cluj-Napoca. Sarcopenia was assessed on MRI images by measuring the psoas muscle area (PMA) at the level of the L4 vertebra before and after neoadjuvant therapy. The primary endpoints were the overall complete response rate (oCR) and acute toxicity. Results : This study included 50 patients with LARC. The oCR rate was 18% and was significantly associated with post-treatment sarcopenia (OR 0.08, p = 0.043). Patients who did not achieve a clinical or pathologic complete response had, on average, an 8% muscle loss during neoadjuvant therapy ( p = 0.022). Cystitis and thrombocytopenia were significantly associated with post-treatment sarcopenia ( p = 0.05 and p = 0.049). Conclusions : Sarcopenia and loss of psoas muscle during neoadjuvant therapy were negatively associated with tumor response in locally advanced rectal cancer. Thrombocytopenia and cystitis are more frequent in sarcopenic than non-sarcopenic patients undergoing neoadjuvant chemoradiation for rectal cancer.

Background and Objectives: Rectal cancer accounts for approximately one-third of colorectal cancers, with over 340,000 deaths globally in 2022. Despite advancements in treatment, the five-year overall survival for locally advanced rectal cancer (LARC) remains at 74%, with significant morbidity. B7H3 (CD276), an immune checkpoint protein, plays a role in tumor progression and resistance to therapy, and correlates with poor prognosis in various cancers, including colorectal cancer. This study aims to evaluate the expression of B7H3 in LARC and its impact on overall complete response (oCR) rates to neoadjuvant therapy., Methods: A retrospective study was conducted on 60 patients with LARC who received neoadjuvant chemoradiation (nCRT) followed by total mesorectal excision (TME). B7H3 expression was assessed using immunohistochemistry on surgical specimens. Expression levels were categorized as high or low based on a composite score, and their association with oCR rates was analyzed., Results: High B7H3 expression was observed in 60% of patients, with 73.5% showing expression in more than 50% of tumor cells. Patients who achieved oCR had significantly lower B7H3 expression compared to those with residual disease ( p < 0.001). No nuclear expression of B7H3 was detected. No significant correlation was found between B7H3 expression and other clinicopathological variables, except for a higher likelihood of non-restorative surgery in patients with elevated B7H3 levels ( p = 0.049). Mucinous adenocarcinoma had high expression of B7H3., Conclusions: Elevated B7H3 expression is associated with reduced oCR rates in LARC, highlighting its potential role as a prognostic biomarker. Further studies with larger cohorts are warranted to validate these findings and explore B7H3-targeted therapies as a treatment strategy for LARC.

The role of magnetic resonance imaging (MRI) in rectal cancer management has significantly increased over the last decade, in line with more personalized treatment approaches. Total neoadjuvant treatment (TNT) plays a pivotal role in the shift from traditional surgical approach to non-surgical approaches such as 'watch-and-wait'. MRI plays a central role in this evolving landscape, providing essential morphological and functional data that support clinical decision-making. Key MRI-based biomarkers, including circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, diffusion-weighted imaging (DWI), and MRI tumour regression grade (mrTRG), have proven valuable for staging, response assessment, and patient prognosis. Functional imaging techniques, such as dynamic contrast-enhanced MRI (DCE-MRI), alongside emerging biomarkers derived from radiomics and artificial intelligence (AI) have the potential to transform rectal cancer management offering data that enhance T and N staging, histopathological characterization, prediction of treatment response, recurrence detection, and identification of genomic features. This review outlines validated morphological and functional MRI-derived biomarkers with both prognostic and predictive significance, while also exploring the potential of radiomics and artificial intelligence in rectal cancer management. Furthermore, we discuss the role of rectal MRI in the 'watch-and-wait' approach, highlighting important practical aspects in selecting patients for non-surgical management.

Background/Objectives : The most important prognostic factors in curatively treated prostate cancer are T and N stage, histology, grade group and initial PSA. A recent study found that men with blood calcium levels at the high end of the normal range are over two-and-a-half times more likely to develop fatal prostate cancer than those with lower calcium levels. However, there is limited evidence regarding the prognostic value of calcium levels at the time of prostate cancer diagnosis. We aimed to determine whether a calcium level in the upper range of normal values has any prognostic value in curatively treated prostate cancer. Methods : We conducted a retrospective analysis of 84 consecutive patients with prostate cancer who underwent curative-intent radiotherapy-either as primary treatment or adjuvant therapy-using external beam radiotherapy with or without brachytherapy. We analyzed all pertinent prognostic factors that could potentially impact disease-free survival. Results : The study revealed that calcium levels at diagnosis significantly predict disease-free survival, whereas the initial PSA level did not hold prognostic significance-likely due to interference from benign prostatic hyperplasia. Conclusions : If our findings are validated, calcium levels at the time of prostate cancer diagnosis could be incorporated into future predictive and prognostic models.

The tumor-to-stroma ratio is a highly debated prognostic factor in the management of several solid tumors and there is no universal agreement on its practicality. In our study, we proposed confirming or dismissing the hypothesis that a simple measurement of stroma quantity is an easy-to-use and strong prognostic tool. We have included 74 consecutive patients with colorectal cancer who underwent primary curative abdominal surgery. The tumors have been grouped into stroma-poor (stroma < 10%), medium-stroma (between 10 and 50%) and stroma-rich (over 50%). The proportion of tumor stroma ranged from 5% to 70% with a median of 25%. Very few, only 6.8% of patients, had stroma-rich tumors, 4% had stroma-poor tumors and 89.2% had tumors with a medium quantity of stroma. The proportion of stroma, at any cut-off, had no statistically significant influence on the disease-specific survival. This can be explained by the low proportion of stroma-rich tumors in our patient group and the inverse correlation between stroma proportion and tumor grade. The real-life proportion of stroma-rich tumors and the complex nature of the stroma-tumor interaction has to be further elucidated.

Currently, only a limited set of molecular traits are utilized to direct treatment for metastatic CRC (mCRC). The molecular classification of CRC depicts tumor heterogeneity based on gene expression patterns and aids in comprehending the biological characteristics of tumor formation, growth and prognosis. Additionally, it assists physicians in tailoring the therapeutic approach. Microsatellite instability (MSI-H)/deficient mismatch repair proteins (MMRd) status has become a ubiquitous biomarker in solid tumors, caused by mutations or methylation of genes and, in turn, the accumulation of mutations and antigens that subsequently induce an immune response. Immune checkpoint inhibitors (ICI) have recently received approval for the treatment of mCRC with MSI-H/MMRd status. However, certain individuals experience either initial or acquired resistance. The tumor-programmed cell death ligand 1 (PD-L1) has been linked to the ability of CRC to evade the immune system and promote its growth. Through comprehensive research conducted via the PUBMED database, the objectives of this paper were to review the molecular characteristics linked to tumor response in metastatic CRC in light of improved patients' outcomes following ICI therapies as seen in clinical trials and to identify particular microRNAs that can modulate the expression of specific oncoproteins, such as PD-L1, and disrupt the mechanisms that allow the immune system to be evaded.

T 2 -weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T 2 W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568-0.776), although the difference was not statistically significant ( p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T 2 W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model's ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications.

Background: Clinical audits are an important tool to objectively assess clinical protocols, procedures, and processes and to detect deviations from good clinical practice. The main aim of this project is to determine adherence to a core set of consensus- based quality indicators and then to compare the institutions in order to identify best practices., Materials and Methods: We conduct a multicentre, international clinical audit of six comprehensive cancer centres in Poland, Spain, Italy, Portugal, France, and Romania as a part of the project, known as IROCATES (Improving Quality in Radiation Oncology through Clinical Audits - Training and Education for Standardization)., Results: Radiotherapy practice varies from country to country, in part due to historical, economic, linguistic, and cultural differences. The institutions developed their own processes to suit their existing clinical practice., Conclusions: We believe that this study will contribute to establishing the value of routinely performing multi-institutional clinical audits and will lead to improvement of radiotherapy practice at the participating centres., Competing Interests: Conflict of interest None declared., (© 2023 Greater Poland Cancer Centre.)

Introduction: We compared the magnetic resonance imaging total tumor volume (TTV) and median apparent diffusion coefficient (ADC) of malignant pleural mesothelioma (MPM) before and at 4 weeks after chemotherapy, to evaluate whether these are potential early markers of treatment response., Methods: Diffusion-weighted magnetic resonance imaging was performed in 23 patients with MPM before and after 4 weeks of chemotherapy. The TTV was measured by semiautomatic segmentation (GrowCut) and transferred onto ADC maps to record the median ADC. Test-retest repeatability of TTV and ADC was evaluated in eight patients. TTV and median ADC changes were compared between responders and nonresponders, defined using modified Response Evaluation Criteria In Solid Tumors on computed tomography (CT) at 12 weeks after treatment. TTV and median ADC were also correlated with CT size measurement and disease survival., Results: The test-retest 95% limits of agreement for TTV were -13.9% to 16.2% and for median ADC -1.2% to 3.3%. A significant increase in median ADC in responders was observed at 4 weeks after treatment ( p  = 0.02). Correlation was found between CT tumor size change at 12 weeks and median ADC changes at 4 weeks post-treatment ( r  = -0.560, p  = 0.006). An increase in median ADC greater than 5.1% at 4 weeks has 100% sensitivity and 90% specificity for responders (area under the curve = 0.933, p < 0.001). There was also moderate correlation between median tumor ADC at baseline and overall survival ( r  = 0.45, p  = 0.03)., Conclusions: Diffusion-weighted magnetic resonance imaging measurements of TTV and median ADC in MPM have good measurement repeatability. Increase in ADC at 4 weeks post-treatment has the potential to be an early response biomarker., (© 2021 The Authors. Published by Elsevier Inc. on behalf of the International Association for the Study of Lung Cancer.)

Purpose: To use deep learning to improve the image quality of subsampled images (number of acquisitions = 1 [NOA 1 ]) to reduce whole-body diffusion-weighted MRI (WBDWI) acquisition times., Materials and Methods: Both retrospective and prospective patient groups were used to develop a deep learning-based denoising image filter (DNIF) model. For initial model training and validation, 17 patients with metastatic prostate cancer with acquired WBDWI NOA 1 and NOA 9 images (acquisition period, 2015-2017) were retrospectively included. An additional 22 prospective patients with advanced prostate cancer, myeloma, and advanced breast cancer were used for model testing (2019), and the radiologic quality of DNIF-processed NOA 1 (NOA 1-DNIF ) images were compared with NOA 1 images and clinical NOA 16 images by using a three-point Likert scale (good, average, or poor; statistical significance was calculated by using a Wilcoxon signed ranked test). The model was also retrained and tested in 28 patients with malignant pleural mesothelioma (MPM) who underwent lung MRI (2015-2017) to demonstrate feasibility in other body regions., Results: The model visually improved the quality of NOA 1 images in all test patients, with the majority of NOA 1-DNIF and NOA 16 images being graded as either "average" or "good" across all image-quality criteria. From validation data, the mean apparent diffusion coefficient (ADC) values within NOA 1-DNIF images of bone disease deviated from those within NOA 9 images by an average of 1.9% (range, 1.1%-2.6%). The model was also successfully applied in the context of MPM; the mean ADCs from NOA 1-DNIF images of MPM deviated from those measured by using clinical-standard images (NOA 12 ) by 3.7% (range, 0.2%-10.6%)., Conclusion: Clinical-standard images were generated from subsampled images by using a DNIF. Keywords: Image Postprocessing, MR-Diffusion-weighted Imaging, Neural Networks, Oncology, Whole-Body Imaging, Supervised Learning, MR-Functional Imaging, Metastases, Prostate, Lung Supplemental material is available for this article. Published under a CC BY 4.0 license., Competing Interests: Disclosures of Conflicts of Interest: K.Z.P. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed no relevant relationships. Other relationships: a patent has been submitted to the UK Intellectual Property Office directly regarding the work described in this article. N.T. disclosed no relevant relationships. A.C. disclosed no relevant relationships. C.M. disclosed no relevant relationships. S.C. disclosed no relevant relationships. D.J.C. disclosed no relevant relationships. J.C.H. disclosed no relevant relationships. Y.J. disclosed no relevant relationships. D.M.K. Activities related to the present article: institution received grant from NIHR Clinical Research Facilities. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. M.D.B. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: consultant for Bayer. Other relationships: a patent has been submitted to the UK Intellectual Property Office directly regarding the work described in this article; a patent has been granted for work in a broadly relevant field (US10885679B2). This patent is also pending in Japan and Europe (JP2019513515A/EP3443373A1)., (2021 by the Radiological Society of North America, Inc.)

Whole-body MRI (WB-MRI) has evolved since its first introduction in the 1970s as an imaging technique to detect and survey disease across multiple sites and organ systems in the body. The development of diffusion-weighted MRI (DWI) has added a new dimension to the implementation of WB-MRI on modern scanners, offering excellent lesion-to-background contrast, while achieving acceptable spatial resolution to detect focal lesions 5 to 10 mm in size. MRI hardware and software advances have reduced acquisition times, with studies taking 40-50 min to complete.The rising awareness of medical radiation exposure coupled with the advantages of MRI has resulted in increased utilization of WB-MRI in oncology, paediatrics, rheumatological and musculoskeletal conditions and more recently in population screening. There is recognition that WB-MRI can be used to track disease evolution and monitor response heterogeneity in patients with cancer. There are also opportunities to combine WB-MRI with molecular imaging on PET-MRI systems to harness the strengths of hybrid imaging. The advent of artificial intelligence and machine learning will shorten image acquisition times and image analyses, making the technique more competitive against other imaging technologies.