Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer

Simple Summary Colorectal cancer is the second most malignant tumor per number of deaths after lung cancer and the third per number of new cases after breast and lung cancer. The correct and rapid identification (i.e., segmentation of the cancer regions) is a fundamental task for correct patient diagnosis. In this study, we propose a novel automated pipeline for the segmentation of MRI scans of patients with LARC in order to predict the response to nCRT using radiomic features. This study involved the retrospective analysis of T-2-weighted MRI scans of 43 patients affected by LARC. The segmentation of tumor areas was on par or better than the state-of-the-art results, but required smaller sample sizes. The analysis of radiomic features allowed us to predict the TRG score, which agreed with the state-of-the-art results. 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

Musculoskeletal oncology and thermal ablation: the current and emerging role of interventional radiology

The role of interventional radiology (IR) is expanding. With new techniques being developed and tested, this radiology subspecialty is taking a step forward in different clinical scenarios, especially in oncology. Musculoskeletal tumoral diseases would definitely benefit from a low-invasive approach that could reduce mortality and morbidity in particular. Thermal ablation through IR has already become important in the palliation and consolidation of bone metastases, oligometastatic disease, local recurrences, and treating specific benign tumors, with a more tailored approach, considering the characteristics of every patient. As image-guided ablation techniques lower their invasiveness and increase their efficacy while the collateral effects and complications decrease, they become more relevant and need to be considered in patient care pathways and clinical management, to improve outcomes. We present a literature review of the different percutaneous and non-invasive image-guided thermal ablation methods that are currently available and that could in the future become relevant to manage musculoskeletal oncologic diseases

Dynamic FDG PET/CT on bladder paraganglioma: A case report

Paraganglioma (PGL) is characterized by equivocal clinical manifestations and arriving to a suspicion might be challenging. Nevertheless, diagnostic imaging and nuclear medicine are a fundamental part of the diagnosis and management of this particular neuroendocrine tumor (NET). We herein report a rare case of bladder paraganglioma with unusual onset and typical PET/CT characteristics that led to its recognition

An Apparent Diffusion Coefficient-based machine learning model can improve Prostate Cancer detection in the grey area of the PI-RADS 3 category: a single-centre experience

The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone non invasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis