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

Magnetic resonance for fluids in porous media: applications to cultural heritage

Nowadays there are several methodologies that offer us the possibility to get information about properties, characteristics and conservation status of materials of interest to Cultural Heritage. However, most of these techniques cannot be carried out at all, because of the destructiveness and/or invasiveness of the method. Nuclear Magnetic Resonance overcomes these difficulties since it can be applied in a non-invasive and non-destructive way especially if it is performed by portable devices. By detecting 1H nuclei of water, analyses to study the conservation status and to have a better knowledge of porous materials are possible. In fact water is the main cause of damage for the artifacts because it may produce dissolution of the binder, mechanical stresses and it is also responsible of transporting aggressive pollutants that might cause acid corrosion. Therefore it is of primary importance to have efficient diagnostic, possibly non-destructive and non-invasive, methods to detect how water affects the artifact in order to plan the most appropriate procedure for the protection and the conservation of the artifact. In this thesis interesting applications to stone, fresco, paint, wood and paper will be presented, proving that NMR is a powerful tool for conducting analyzes in the field of Cultural Heritage

Implementation of an automated pipeline to predict the response to neoadjuvant chemo-radiotherapy of patients affected by colorectal cancer

Colorectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of one of the cells making up the colorectal tract. In order to get information about diagnosis, therapy evaluation on colorectal cancer, analysis on radiological images can be performed through the application of dedicated algorithms. Up to now, this process is performed using manual or semi-automatic techniques, which are time-consuming and highly operator dependent. The aim of this project is to develop and apply an automated pipeline to predict the response to neoadjuvant chemo-radiotherapy of patients affected by colorectal cancer. Here, we propose an approach based on automatic segmentation and radiomic features extraction. The segmentation process exploits a Convolutional Neural Network like U-Net, trained with medical annotations to perform the segmentation of the tumor areas. Then, from the segmented regions, radiomic features are extracted and analyzed to obtain the prediction of response, based on the Tumor Regression Grade (TRG). We tested and developed our pipeline on MRI scans provided by the IRCCS Sant’Orsola-Malpighi Polyclinic. The performance of the pipeline was measured for the segmentation purpose and for the prediction of response. The results of these preliminary tests show that the pipeline is able to achieve a segmentation consistent with the medical annotations and a Dice Similarity Coefficient (DSC) coherent with literature. Even for the prediction of response, the results show that the pipeline is able to correctly classify most of the cases