A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study.

The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-Net structure. It is a multicenter study, including 3 different Italian institutions, that used 4 different MRI scanners. Two of them were used for training and tuning the systems, while the other two for the validation. The implemented algorithm consists of a pre-processing step to normalize and to highlight the tumoral area, followed by the CRC segmentation using different U-net structures. Automatic masks were compared with manual segmentations performed by three experienced radiologists, one at each center. The two best performing systems (called mdl2 and mdl3), obtained a median Dice Similarity Coefficient of 0.68(mdl2) - 0.69(mdl3), precision of 0.75(md/2) - 0.71(md/3), and recall of 0.69(mdl2) - 0.73(mdl3) on the validation set. Both systems reached high detection rates, 0.98 and 0.95, respectively, on the validation set. These encouraging results, if confirmed on larger dataset, might improve the management of patients with CRC, since it can be used as a fast and precise tool for further radiomics analyses. Clinical Relevance - To provide a reliable tool able to automatically segment CRC tumors that can be used as first step in future radiomics studies aimed at predicting response to chemotherapy and personalizing treatment.

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study.

BACKGROUND: Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15-30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. METHODS: Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. RESULTS: Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. CONCLUSION: Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations.

Predictive role of diffusion-weighted whole-body MRI (DW-MRI) imaging response according to MY-RADS criteria after autologous stem cell transplantation in patients with multiple myeloma and combined evaluation with MRD assessment by flow cytometry.

BACKGROUND: Diffusion-weighted whole-body MRI (DW-MRI) is increasingly used in the management of multiple myeloma (MM) patients, but data regarding the prognostic role of DW-MRI imaging response after treatment are lacking. The Myeloma Response Assessment and Diagnosis System (MY-RADS) imaging recommendations recently proposed the criteria for response assessment category (RAC) with a 5-point scale in order to standardize response assessment after therapy, but this score still needs to be validated. METHODS: We investigated the prognostic role of RAC criteria in 64 newly diagnosed MM patients after autologous stem cell transplantation (ASCT), and we combined the results of MY-RADS with those of minimal residual disease (MRD) assessment by multiparametric flow cytometry (MFC). RESULTS: Superior post-ASCT PFS and OS were observed in patients with complete imaging response (RAC1), with respect to patients with imaging residual disease (RAC≥2): median PFS not reached (NR) versus 26.5 months, p = 0.0047, HR 0.28 (95% CI: 0.12-0.68); 3-year post-ASCT OS 92% versus 69% for RAC1 versus RAC ≥2, respectively, p = 0.047, HR 0.24 (95% CI: 0.06-0.99). Combining MRD and imaging improved prediction of outcome, with double-negative and double-positive features defining groups with excellent and dismal PFS, respectively (PFS NR vs. 10.6 months); p = 0.001, HR 0.07 (95%CI: 0.01-0.36). CONCLUSION: The present study supports the applicability of MY-RADS recommendations after ASCT; RAC criteria were able to independently stratify patients and to better predict their prognosis and the combined use of DW-MRI with MFC allowed a more precise evaluation of MRD.