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      Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study

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          Abstract

          Background

          Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics.

          Methods

          We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months’ postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram.

          Findings

          DLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM ( P < 0·05).

          Interpretation

          MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT.

          Funding

          The funding bodies that contributed to this study are listed in the Acknowledgements section.

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          Most cited references44

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          Deep learning.

          Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)
          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Dermatologist-level classification of skin cancer with deep neural networks

            Andre Esteva, Brett Kuprel, Roberto Chacón-Novoa … (2017)
            Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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              A survey on deep learning in medical image analysis

              Geert Litjens, Arnaud Arindra Adiyoso Setio, Thijs Kooi … (2017)
              Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.
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                Author and article information

                Contributors
                Tong Tong
                Xiaochun Meng
                Jie Tian
                Journal
                Journal ID (nlm-ta): EBioMedicine
                Journal ID (iso-abbrev): EBioMedicine
                Title: EBioMedicine
                Publisher: Elsevier
                ISSN (Electronic): 2352-3964
                Publication date PMC-release: 20 June 2021
                Publication date Collection: July 2021
                Publication date (Electronic): 20 June 2021
                Volume: 69
                Electronic Location Identifier: 103442
                Affiliations
                [a ]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
                [b ]Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
                [c ]CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
                [d ]CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
                [e ]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100080, China
                [f ]Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, China
                [g ]Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
                [h ]Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China
                [i ]Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
                [j ]Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing 100191, China
                Author notes
                Article
                Publisher Item ID: S2352-3964(21)00235-8 Publisher ID: 103442
                DOI: 10.1016/j.ebiom.2021.103442
                PMC ID: 8237293
                PubMed ID: 34157487
                SO-VID: c932e589-7f56-450a-b26d-71fd502e69de
                Copyright © © 2021 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 3 March 2021
                Date revision received : 17 April 2021
                Date accepted : 1 June 2021
                Categories
                Subject: Research paper

                Keywords: locally advanced rectal cancer,distant metastasis,neoadjuvant chemoradiotherapy,deep learning radiomics,magnetic resonance imaging
                Data availability:
                Keywords: locally advanced rectal cancer, distant metastasis, neoadjuvant chemoradiotherapy, deep learning radiomics, magnetic resonance imaging

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