Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study

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Abstract

Background

The aim of this work is to combine radiological and pathological information of tumor to develop a signature for pretreatment prediction of discrepancies of pathological response at several centers and restage patients with locally advanced rectal cancer (LARC) for individualized treatment planning.

Patients and Methods

A total of 981 consecutive patients with evaluation of response according to tumor regression grade (TRG) who received nCRT were retrospectively recruited from four hospitals (primary cohort and external validation cohort 1–3); both pretreatment multiparametric MRI (mp-MRI) and whole slide image (WSI) of biopsy specimens were available for each patient. Quantitative image features were extracted from mp-MRI and WSI and used to construct a radiopathomics signature (RPS) powered by an artificial-intelligence model. Models based on mp-MRI or WSI alone were also constructed for comparison.

Results

The RPS showed overall accuracy of 79.66–87.66% in validation cohorts. The areas under the curve of RPS at specific response grades were 0.98 (TRG0), 0.93 (≤ TRG1), and 0.84 (≤ TRG2). RPS at each grade of pathological response revealed significant improvement compared with both signatures constructed without combining multiscale tumor information ( P < 0.01). Moreover, RPS showed relevance to distinct probabilities of overall survival and disease-free survival in patients with LARC who underwent nCRT ( P < 0.05).

Conclusions

The results of this study suggest that radiopathomics, combining both radiological information of the whole tumor and pathological information of local lesions from biopsy, could potentially predict discrepancies of pathological response prior to nCRT for better treatment planning.

Electronic supplementary material

The online version of this article (10.1245/s10434-020-08659-4) contains supplementary material, which is available to authorized users.

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Most cited references 26

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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Navneet Narula, Theodore Sakellaropoulos, Paolo Ocampo … (2018)

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .

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Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

Cui-Shan Liang, Jie Tian, Lan He … (2016)

To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).

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Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Jakob Kather, Alexander T. Pearson, Niels Halama … (2019)

Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer.

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Author and article information

Contributors

Ruihua Xu: xurh@sysucc.org.cn

Xinjuan Fan: fanxjuan@mail.sysu.edu.cn

Jie Tian:

ORCID: http://orcid.org/0000-0003-0498-0432

jie.tian@ia.ac.cn

Journal

Journal ID (nlm-ta): Ann Surg Oncol

Journal ID (iso-abbrev): Ann. Surg. Oncol

Title: Annals of Surgical Oncology

Publisher: Springer International Publishing (Cham )

ISSN (Print): 1068-9265

ISSN (Electronic): 1534-4681

Publication date (Electronic): 29 July 2020

Publication date PMC-release: 29 July 2020

Publication date (Print): 2020

Volume: 27

Issue: 11

Pages: 4296-4306

Affiliations

[1 ]GRID grid.263826.b, ISNI 0000 0004 1761 0489, School of Computer Science and Engineering, , Southeast University, ; Nanjing, China

[2 ]GRID grid.429126.a, ISNI 0000 0004 0644 477X, CAS Key Laboratory of Molecular Imaging, , Institute of Automation, ; Beijing, China

[3 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, School of Artificial Intelligence, , University of Chinese Academy of Sciences, ; Beijing, China

[4 ]GRID grid.488525.6, Department of Radiation Oncology, , The Sixth Affiliated Hospital of Sun Yat-sen University, ; Guangzhou, China

[5 ]GRID grid.488525.6, Department of Pathology, , The Sixth Affiliated Hospital of Sun Yat-sen University, ; Guangzhou, China

[6 ]GRID grid.452826.f, Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, , The Third Affiliated Hospital of Kunming Medical University, ; Kunming, China

[7 ]GRID grid.412474.0, ISNI 0000 0001 0027 0586, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, , Peking University Cancer Hospital & Institute, ; Beijing, China

[8 ]GRID grid.440736.2, ISNI 0000 0001 0707 115X, Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, , Xidian University, ; Xi’an, China

[9 ]GRID grid.452826.f, Department of Pathology, Yunnan Cancer Center, Yunnan Cancer Hospital, , The Third Affiliated Hospital of Kunming Medical University, ; Kunming, China

[10 ]GRID grid.263826.b, ISNI 0000 0004 1761 0489, LIST, Key Laboratory of Computer Network and Information Integration, , Southeast University, Ministry of Education, ; Nanjing, China

[11 ]GRID grid.488530.2, ISNI 0000 0004 1803 6191, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, , Sun Yat-sen University Cancer Center, ; Guangzhou, China

[12 ]GRID grid.64939.31, ISNI 0000 0000 9999 1211, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, , Beihang University, ; Beijing, China

Author information

Jie Tian http://orcid.org/0000-0003-0498-0432

Article

Publisher ID: 8659

DOI: 10.1245/s10434-020-08659-4

PMC ID: 7497677

PubMed ID: 32729045

SO-VID: 1dc29279-cd76-4956-a29a-bcacda7404df

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 3 January 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100002367, Chinese Academy of Sciences;

Award ID: QYZDJ-SSW-JSC005

Award ID: KFJ-STS-ZDTP-059

Funded by: Youth Innovation Promotion Association CAS

Award ID: 2019136

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 81922040

Award ID: 81930053

Award ID: 81527805

Award ID: 81772012

Funded by: Beijing Natural Science Foundation

Award ID: 7182109

Funded by: National Key R&D Program of China

Award ID: 2017YFA0205200

Award ID: 2017YFA0700401

Award ID: 2016YFA0100902

Funded by: Strategic Priority Research Program of Chinese Academy of Sciences

Award ID: XDB32030200

Award ID: XDB01030200

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ScienceOpen disciplines: Oncology & Radiotherapy

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ScienceOpen disciplines: Oncology & Radiotherapy

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Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study

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Abstract

Background

Patients and Methods

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Electronic supplementary material

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Karger: Oncology

Most cited references 26

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

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