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      Predicting Treatment Response of Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Using Amide Proton Transfer MRI Combined With Diffusion-Weighted Imaging

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          Abstract

          Objective

          To evaluate amide proton weighted (APTw) MRI combined with diffusion-weighted imaging (DWI) in predicting neoadjuvant chemoradiotherapy (NCRT) response in patients with locally advanced rectal cancer (LARC).

          Methods

          53 patients with LARC were enrolled in this retrospective study. MR examination including APTw MRI and DWI was performed before and after NCRT. APTw SI, ADC value, tumor size, CEA level before and after NCRT were assessed. The difference of the above parameters between before and after NCRT was calculated. The tumor regression grading (TRG) was assessed by American Joint Committee on Cancer’s Cancer Staging Manual AJCC 8th score. The Shapiro-Wilk test, paired t-test and Wilcoxon Signed Ranks test, two-sample t-test, Mann-Whitney U test and multivariate analysis were used for statistical analysis.

          Results

          Of the 53 patients, 19 had good responses (TRG 0-1), 34 had poor responses (TRG 2-3). After NCRT, all the rectal tumors demonstrated decreased APT values, increased ADC values, reduced tumor volumes and CEA levels (all p < 0.001). Good responders demonstrated higher pre-APT values, higher Δ APT values, lower pre- ADC values and higher Δ tumor volumes than poor responders. Pre-APT combined with pre-ADC achieved the best diagnostic performance, with AUC of 0.895 (sensitivity of 85.29%, specificity of 89.47%, p < 0.001) in predicting good response to NCRT.

          Conclusion

          The combination of APTw and DWI may serve as a noninvasive biomarker for evaluating and identifying response to NCRT in LARC patients.

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          Most cited references44

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          Opportunities and obstacles for deep learning in biology and medicine

          Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
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            A watch-and-wait approach for locally advanced rectal cancer after a clinical complete response following neoadjuvant chemoradiation: a systematic review and meta-analysis.

            A watch-and-wait approach for patients with clinical complete response to neoadjuvant chemoradiation could avoid the morbidity of conventional surgery for rectal cancer. However, the safety of this approach is unclear. We synthesised the evidence for watch-and-wait as a treatment for rectal cancer.
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              Amide proton transfer (APT) contrast for imaging of brain tumors.

              In this work we demonstrate that specific MR image contrast can be produced in the water signal that reflects endogenous cellular protein and peptide content in intracranial rat 9L gliosarcomas. Although the concentration of these mobile proteins and peptides is only in the millimolar range, a detection sensitivity of several percent on the water signal (molar concentration) was achieved. This was accomplished with detection sensitivity enhancement by selective radiofrequency (RF) labeling of the amide protons, and by utilizing the effective transfer of this label to water via hydrogen exchange. Brain tumors were also assessed by conventional T(1)-weighted, T(2)-weighted, and diffusion-weighted imaging. Whereas these commonly-used approaches yielded heterogeneous images, the new amide proton transfer (APT) technique showed a single well-defined region of hyperintensity that was assigned to brain tumor tissue. Copyright 2003 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                01 July 2021
                2021
                : 11
                : 698427
                Affiliations
                [1] 1 Department of Radiology, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine , Guangzhou, China
                [2] 2 Department of Pathology, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine , Guangzhou, China
                Author notes

                Edited by: Pierfrancesco Franco, University of Eastern Piedmont, Italy

                Reviewed by: Luca Boldrini, Dipartimento di Radioterapia Oncologica, Fondazione Policlinico A. Gemelli IRCCS, Italy; Luciana Caravatta, SS Annunziata Polyclinic Hospital, Chieti, Italy

                *Correspondence: Xian Liu, liuxian74@ 123456hotmail.com

                This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology

                Article
                10.3389/fonc.2021.698427
                8281887
                34277445
                2b4e4761-d641-4878-af67-d6f327f48373
                Copyright © 2021 Chen, Mao, Li, Wei, Hu, Ye, Feng, Liu and Liu

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 21 April 2021
                : 03 June 2021
                Page count
                Figures: 7, Tables: 4, Equations: 3, References: 44, Pages: 12, Words: 5284
                Categories
                Oncology
                Original Research

                Oncology & Radiotherapy
                amide proton transfer,diffusion-weighted imaging,neoadjuvant chemoradiotherapy,locally advanced rectal cancer (larc),treatment response

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