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Grant Details

Grant Number: 5U01CA269181-02 Interpret this number
Primary Investigator: Madabhushi, Anant
Organization: Emory University
Project Title: An Ai-Enabled Digital Pathology Platform for Multi-Cancer Diagnosis, Prognosis and Prediction of Therapeutic Benefit
Fiscal Year: 2023


SUMMARY: Recognizing that over-diagnosis of many cancers is leading to over-treatment with adjuvant chemotherapy or with radiation therapy boost, there is a growing appreciation for the need for prognostic and predictive assays to identify those cancer patients who can benefit from therapy de-intensification. While multi-gene-expression based tests such as Oncotype DX and Decipher exist for identifying early-stage breast and prostate cancer patients who could avoid adjuvant therapies and hence mitigate side-effects and complications, the price of these tests ($3K-4K/patient) puts them beyond the reach of most patients in low- and middle-income countries (LMICs). Ironically, the need for these prognostic and predictive tests is even more acute in LMICs like India, where access to treatment resources like radiation and chemotherapy are limited and hence need to be administered judiciously to those patients who stand to receive the most benefit from them. Sophisticated digital pathomic analysis with computer vision and pattern recognition tools has been shown to “unlock” sub-visual attributes about tumor behavior and patient outcomes from hematoxylin & eosin (H&E)-stained slides alone. The Madabhushi team at Case Western Reserve University (CWRU) has extensively shown the potential for these approaches for predicting outcome and therapeutic response for breast, head and neck, lung and prostate cancer. The Madabhushi team working with collaborators Dr. Parmar and Dr. Desai at the Tata Memorial Center (TMC), the largest cancer center in India, has shown that advanced pathomic analysis is able to identify unique prognostic morphologic signatures of breast cancer that are different between South Asian (SA) and Caucasian American (CA) women 1. In addition, the CWRU group has shown that digital pathomic based image classifiers can significantly improve and even outperform the prognostic and predictive performance of expensive gene-expression assays for breast (Oncotype Dx) and prostate cancer (Decipher) 2. Building on the strong extant collaboration between CWRU and TMC 3, and a strong track record in digital image based prognostic and predictive based assays, we propose to optimize and validate an AI-enabled Digital Pathology Platform (ADAPT) for Multi-Cancer Diagnosis, Prognosis and Prediction of Therapeutic Benefit. ADAPT will involve optimizing the previously developed image assays by the CWRU group in the context of SA cancer patients. Furthermore, by integrating the AI-pathomic tools with PathPresenter, a widely used digital pathology image analysis platform, ADAPT will have a global footprint for the prognostic and predictive tools. Specifically, ADAPT will be validated for predicting outcome and benefit of adjuvant chemo- and radiation therapy in the context of estrogen receptor positive (ER+) breast cancer (BC) and triple negative breast cancer (TNBC), oral cavity squamous cell carcinoma (OC-SCC) and prostate cancer at TMC via a number of clinical trial datasets in the US (SWOG S8814, RTOG 0920, 0521) and at TMC (AREST, POP-RT). Successful project completion will establish ADAPT as an Affordable Precision Medicine (APM) solution for Indian cancer patients.


Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features.
Authors: Chen C. , Lu C. , Viswanathan V. , Maveal B. , Maheshwari B. , Willis J. , Madabhushi A. .
Source: The journal of pathology. Clinical research, 2023-10-11; , .
EPub date: 2023-10-11.
PMID: 37822044
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Novel Fractal-Based Sub-RPE Compartment OCT Radiomics Biomarkers Are Associated With Subfoveal Geographic Atrophy in Dry AMD.
Authors: Kar S.S. , Cetin H. , Abraham J. , Srivastava S.K. , Whitney J. , Madabhushi A. , Ehlers J.P. .
Source: IEEE transactions on bio-medical engineering, 2023 Oct; 70(10), p. 2914-2921.
EPub date: 2023-09-27.
PMID: 37097804
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Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.
Authors: Thagaard J. , Broeckx G. , Page D.B. , Jahangir C.A. , Verbandt S. , Kos Z. , Gupta R. , Khiroya R. , Abduljabbar K. , Acosta Haab G. , et al. .
Source: The Journal of pathology, 2023 Aug; 260(5), p. 498-513.
EPub date: 2023-08-23.
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Source: The Journal of pathology, 2023 Aug; 260(5), p. 514-532.
EPub date: 2023-08-23.
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Machine learning driven index of tumor multinucleation correlates with survival and suppressed anti-tumor immunity in head and neck squamous cell carcinoma patients.
Authors: Koyuncu C.F. , Frederick M.J. , Thompson L.D.R. , Corredor G. , Khalighi S. , Zhang Z. , Song B. , Lu C. , Nag R. , Sankar Viswanathan V. , et al. .
Source: Oral oncology, 2023 Aug; 143, p. 106459.
EPub date: 2023-06-10.
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Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies.
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Source: Kidney360, 2023-05-01; 4(5), p. 648-658.
EPub date: 2023-04-05.
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Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer.
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Source: Medical image analysis, 2023 Feb; 84, p. 102702.
EPub date: 2022-11-24.
PMID: 36516556
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Image analysis reveals differences in tumor multinucleations in Black and White patients with human papillomavirus-associated oropharyngeal squamous cell carcinoma.
Authors: Koyuncu C.F. , Nag R. , Lu C. , Corredor G. , Viswanathan V.S. , Sandulache V.C. , Fu P. , Yang K. , Pan Q. , Zhang Z. , et al. .
Source: Cancer, 2022-11-01; 128(21), p. 3831-3842.
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Evaluating the utility of deep learning for predicting therapeutic response in diabetic eye disease.
Authors: Dong V. , Sevgi D.D. , Kar S.S. , Srivastava S.K. , Ehlers J.P. , Madabhushi A. .
Source: Frontiers in ophthalmology, 2022; 2, .
EPub date: 2022-08-12.
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An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study.
Authors: Vaidya P. , Alilou M. , Hiremath A. , Gupta A. , Bera K. , Furin J. , Armitage K. , Gilkeson R. , Yuan L. , Fu P. , et al. .
Source: Frontiers in radiology, 2022; 2, .
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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises.
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Source: Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2021 May; 109(5), p. 820-838.
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