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Iyer JS, Pokkalla H, Biddle-Snead C, Carrasco-Zevallos O, Lin M, Shanis Z, Le Q, Juyal D, Pouryahya M, Pedawi A, Hoffman S, Elliott H, Leidal K, Myers RP, Chung C, Billin AN, Watkins TR, Resnick M, Wack K, Glickman J, Burt AD, Loomba R, Sanyal AJ, Montalto MC, Beck AH, Taylor-Weiner A, Wapinski I. AI-based histologic scoring enables automated and reproducible assessment of enrollment criteria and endpoints in NASH clinical trials. medRxiv 2023:2023.04.20.23288534. [PMID: 37162870 PMCID: PMC10168404 DOI: 10.1101/2023.04.20.23288534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Clinical trials in nonalcoholic steatohepatitis (NASH) require histologic scoring for assessment of inclusion criteria and endpoints. However, guidelines for scoring key features have led to variability in interpretation, impacting clinical trial outcomes. We developed an artificial intelligence (AI)-based measurement (AIM) tool for scoring NASH histology (AIM-NASH). AIM-NASH predictions for NASH Clinical Research Network (CRN) grades of necroinflammation and stages of fibrosis aligned with expert consensus scores and were reproducible. Continuous scores produced by AIM-NASH for key histological features of NASH correlated with mean pathologist scores and with noninvasive biomarkers and strongly predicted patient outcomes. In a retrospective analysis of the ATLAS trial, previously unmet pathological endpoints were met when scored by the AIM-NASH algorithm alone. Overall, these results suggest that AIM-NASH may assist pathologists in histologic review of NASH clinical trials, reducing inter-rater variability on trial outcomes and offering a more sensitive and reproducible measure of patient therapeutic response.
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Affiliation(s)
| | | | | | - Oscar Carrasco-Zevallos
- PathAI, Boston, MA, USA
- Affiliation shown is that during the time of study; current affiliation is Johnson & Johnson, New Brunswick, NJ, USA
| | | | | | | | | | - Maryam Pouryahya
- PathAI, Boston, MA, USA
- Affiliation shown is that during the time of study; current affiliation is AstraZeneca, Gaithersburg, MD, USA
| | - Aryan Pedawi
- PathAI, Boston, MA, USA
- Affiliation shown is that during the time of study; current affiliation is Atomwise, San Francisco, CA, USA
| | | | - Hunter Elliott
- PathAI, Boston, MA, USA
- Affiliation shown is that during the time of study; current affiliation is BigHat Biosciences, San Mateo, CA, USA
| | - Kenneth Leidal
- PathAI, Boston, MA, USA
- Affiliation shown is that during the time of study; current affiliation is Genesis Therapeutics, Burlingame, CA, USA
| | - Robert P. Myers
- Gilead Sciences, Inc., Foster City, CA, USA
- Affiliation shown is that during the time of study; current affiliation is OrsoBio, Inc., Palo Alto, CA, USA
| | - Chuhan Chung
- Gilead Sciences, Inc., Foster City, CA, USA
- Affiliation shown is that during the time of study; current affiliation is Inipharm, San Diego, CA, USA
| | | | | | - Murray Resnick
- PathAI, Boston, MA, USA
- Affiliation shown is that during the time of study; current affiliation is Rhode Island Hospital and The Miriam Hospital, Providence, RI, USA
| | | | | | | | - Rohit Loomba
- NAFLD Research Center, Division of Gastroenterology and Hepatology, University of California at San Diego, San Diego, CA, USA
| | - Arun J. Sanyal
- Stravitz-Sanyal Institute for Liver Disease and Metabolic Health, VCU School of Medicine, Richmond, VA, USA
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Bosch J, Chung C, Carrasco-Zevallos OM, Harrison SA, Abdelmalek MF, Shiffman ML, Rockey DC, Shanis Z, Juyal D, Pokkalla H, Le QH, Resnick M, Montalto M, Beck AH, Wapinski I, Han L, Jia C, Goodman Z, Afdhal N, Myers RP, Sanyal AJ. A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis. Hepatology 2021; 74:3146-3160. [PMID: 34333790 DOI: 10.1002/hep.32087] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND AND AIMS The hepatic venous pressure gradient (HVPG) is the standard for estimating portal pressure but requires expertise for interpretation. We hypothesized that HVPG could be extrapolated from liver histology using a machine learning (ML) algorithm. APPROACH AND RESULTS Patients with NASH with compensated cirrhosis from a phase 2b trial were included. HVPG and biopsies from baseline and weeks 48 and 96 were reviewed centrally, and biopsies evaluated with a convolutional neural network (PathAI, Boston, MA). Using trichrome-stained biopsies in the training set (n = 130), an ML model was developed to recognize fibrosis patterns associated with HVPG, and the resultant ML HVPG score was validated in a held-out test set (n = 88). Associations between the ML HVPG score with measured HVPG and liver-related events, and performance of the ML HVPG score for clinically significant portal hypertension (CSPH) (HVPG ≥ 10 mm Hg), were determined. The ML-HVPG score was more strongly correlated with HVPG than hepatic collagen by morphometry (ρ = 0.47 vs. ρ = 0.28; P < 0.001). The ML HVPG score differentiated patients with normal (0-5 mm Hg) and elevated (5.5-9.5 mm Hg) HVPG and CSPH (median: 1.51 vs. 1.93 vs. 2.60; all P < 0.05). The areas under receiver operating characteristic curve (AUROCs) (95% CI) of the ML-HVPG score for CSPH were 0.85 (0.80, 0.90) and 0.76 (0.68, 0.85) in the training and test sets, respectively. Discrimination of the ML-HVPG score for CSPH improved with the addition of a ML parameter for nodularity, Enhanced Liver Fibrosis, platelets, aspartate aminotransferase (AST), and bilirubin (AUROC in test set: 0.85; 95% CI: 0.78, 0.92). Although baseline ML-HVPG score was not prognostic, changes were predictive of clinical events (HR: 2.13; 95% CI: 1.26, 3.59) and associated with hemodynamic response and fibrosis improvement. CONCLUSIONS An ML model based on trichrome-stained liver biopsy slides can predict CSPH in patients with NASH with cirrhosis.
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Affiliation(s)
- Jaime Bosch
- Department of Biomedical Research, University of Bern, Bern, Switzerland
- University of Barcelona-IDIBAPS and CIBERehd, Barcelona, Spain
| | | | | | | | | | | | - Don C Rockey
- Medical University of South Carolina, Charleston, SC
| | | | | | | | | | | | | | | | | | - Ling Han
- Gilead Sciences, Inc, Foster City, CA
| | | | | | - Nezam Afdhal
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
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Taylor‐Weiner A, Pokkalla H, Han L, Jia C, Huss R, Chung C, Elliott H, Glass B, Pethia K, Carrasco‐Zevallos O, Shukla C, Khettry U, Najarian R, Taliano R, Subramanian GM, Myers RP, Wapinski I, Khosla A, Resnick M, Montalto MC, Anstee QM, Wong VW, Trauner M, Lawitz EJ, Harrison SA, Okanoue T, Romero‐Gomez M, Goodman Z, Loomba R, Beck AH, Younossi ZM. A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH. Hepatology 2021; 74:133-147. [PMID: 33570776 PMCID: PMC8361999 DOI: 10.1002/hep.31750] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 12/23/2020] [Accepted: 01/05/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND AIMS Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response. APPROACH AND RESULTS Here, we describe a machine learning (ML)-based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological features in NASH, including steatosis, inflammation, hepatocellular ballooning, and fibrosis. The ML-based predictions showed strong correlations with expert pathologists and were prognostic of progression to cirrhosis and liver-related clinical events. We developed a heterogeneity-sensitive metric of fibrosis response, the Deep Learning Treatment Assessment Liver Fibrosis score, which measured antifibrotic treatment effects that went undetected by manual pathological staging and was concordant with histological disease progression. CONCLUSIONS Our ML method has shown reproducibility and sensitivity and was prognostic for disease progression, demonstrating the power of ML to advance our understanding of disease heterogeneity in NASH, risk stratify affected patients, and facilitate the development of therapies.
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Affiliation(s)
| | | | - Ling Han
- Gilead Sciences, Inc.Foster CityCA
| | | | | | | | | | | | | | | | | | | | | | - Ross Taliano
- Warren Alpert Medical School of Brown UniversityProvidenceRI
| | | | | | | | | | - Murray Resnick
- PathAIBostonMA,Warren Alpert Medical School of Brown UniversityProvidenceRI
| | | | - Quentin M. Anstee
- Translational & Clinical Research Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Vincent Wai‐Sun Wong
- Department of Medicine and TherapeuticsThe Chinese University of Hong KongHong KongHong Kong
| | - Michael Trauner
- Division of Gastroenterology and HepatologyMedical University of ViennaViennaAustria
| | | | | | | | | | - Zachary Goodman
- Department of MedicineInova Fairfax Medical CampusFalls ChurchVA,Betty and Guy Beatty Center for Integrated ResearchInova Health SystemFalls ChurchVA
| | - Rohit Loomba
- NAFLD Research CenterUniversity of California at San DiegoLa JollaCA
| | | | - Zobair M. Younossi
- Department of MedicineInova Fairfax Medical CampusFalls ChurchVA,Betty and Guy Beatty Center for Integrated ResearchInova Health SystemFalls ChurchVA
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Kerner JK, Cleary A, Jain S, Pokkalla H, Glass B, Grossmith S, Harary M, Mittendorf E, Beck AH, Khosla A, Schnitt SJ, Wapinski I, King T. Abstract P5-02-02: Artificial intelligence powered predictive analysis of atypical ductal hyperplasia from digitized pathology images. Cancer Res 2020. [DOI: 10.1158/1538-7445.sabcs19-p5-02-02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Approximately 15-25% of patients with atypical ductal hyperplasia (ADH) diagnosed on breast core needle biopsy (CNB) are upgraded to ductal carcinoma in situ (DCIS) or invasive carcinoma (IC) on surgical excision. The reproducible identification of patients with ADH on CNB who are more likely to have upgrades at excision remains elusive. We hypothesized that a machine learning approach could be utilized to train models to recognize ADH on digitized pathology images and to identify cases of ADH more likely to be upgraded to DCIS or IC at excision. The purpose of this study was to determine the accuracy of the machine learning approach to identify ADH.
Methods: 726 digitized images of CNB slides derived from 306 cases with a diagnosis of ADH between 11/2004-3/2018 were included in this study. Independent histologic review by two breast pathologists identified slides with and without ADH from each case. 39 board certified pathologists with experience in evaluation of breast biopsies were employed for tissue region annotation on the PathAI research platform (not intended for diagnostic purposes), yielding 14,118 tissue region annotations. Region annotations included ADH, ADH stroma, flat epithelial atypia (FEA), lobular neoplasia (LN), calcifications (Ca), columnar cell change/hyperplasia, sclerosing adenosis, papilloma, normal terminal duct lobular units and other non-atypical breast tissue regions. These annotations were used to train a convolutional neural network (CNN) with 35 layers and approximately 9 million parameters to identify ADH. The data were split into training and testing sets, representing 61.1% and 38.9% of the data respectively. The distribution of cases, images with ADH and cases with upgrade were balanced between the training and testing sets.
Results: CNB specimens were assigned labels of “ADH” or “No ADH” based on histologic assessment. AI models were able to predict the diagnosis of ADH with 85% sensitivity (144 of 168 images within the test set) and 69% specificity (78 of 113 images within the test set). The slide-level area under the receiver operator curve (ROC) for this model was 0.84.
Conclusions: A deep learning-based classifier showed strong performance for the identification of ADH from whole slide images of H&E stained breast CNBs. With further development, this approach may improve the reproducibility and standardization of the diagnosis of ADH. Future analyses will focus on determining if morphologic features of ADH extracted by the deep learning system can be used to predict upgrade to DCIS and IC. This approach may help stratify patients with ADH on CNB into those who require surgical excision and those who can be followed with active surveillance.
Citation Format: Jennifer K. Kerner, Allison Cleary, Suyog Jain, Harsha Pokkalla, Benjamin Glass, Sam Grossmith, Maya Harary, Elizabeth Mittendorf, Andrew H. Beck, Aditya Khosla, Stuart J. Schnitt, Ilan Wapinski, Tari King. Artificial intelligence powered predictive analysis of atypical ductal hyperplasia from digitized pathology images [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-02-02.
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Affiliation(s)
| | - Allison Cleary
- 2Dana Farber / Brigham and Women’s Cancer Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | | | | | | | - Sam Grossmith
- 3Dana Farber / Brigham and Women’s Cancer Center, Boston, MA
| | - Maya Harary
- 4Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Elizabeth Mittendorf
- 2Dana Farber / Brigham and Women’s Cancer Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | | | | | - Stuart J. Schnitt
- 2Dana Farber / Brigham and Women’s Cancer Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | | | - Tari King
- 2Dana Farber / Brigham and Women’s Cancer Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
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Szabo PM, Lee G, Ely S, Baxi V, Pokkalla H, Elliott H, Wang D, Glass B, Kerner JK, Wapinski I, Hedvat C, Locke D, Pandya D, Adya N, Qi Z, Greenfield A, Edwards R, Montalto M. CD8+ T cells in tumor parenchyma and stroma by image analysis (IA) and gene expression profiling (GEP): Potential biomarkers for immuno-oncology (I-O) therapy. J Clin Oncol 2019. [DOI: 10.1200/jco.2019.37.15_suppl.2594] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
2594 Background: Distribution patterns of CD8+ T cells within the tumor microenvironment (TME) can be assessed by IA, which may reflect underlying tumor biology and serve as a potential biomarker to assess the utility of I-O therapy. These patterns are variable and may be classified as immune desert (minimal infiltrate), excluded (T cells confined to tumor stroma or to the invasive margin), or inflamed (T cells diffusely infiltrating tumor parenchyma and stroma). We hypothesized that association of a GEP signature with abundance of parenchymal and stromal T-cell infiltrates may identify biomarkers of response or resistance to I-O therapy. To test this, we applied an AI-powered IA platform to quantify CD8+ T cells by geographical location and used GEP to define both CD8 abundance and associated geographic localization to tumor parenchyma and stroma. Methods: We performed an analysis using a tumor inflammatory GEP assay and CD8 immunohistochemistry on procured specimens (335 melanoma, 391 SCCHN). Digitized slides were used to train a convolutional neural network to quantify the number of CD8+ T cells in stroma, tumor parenchyma, parenchyma-stromal interface, and invasive margin. Generalized constrained regression models were used to predict GEP signatures specifically for stromal and parenchymal CD8+ T cells. Results: Parenchymal and stromal GEP scores were highly concordant with CD8+ infiltrate geography (adj- r2: 0.67, 0.65, respectively; P ≤ 0.01). Little overlap existed between gene sets associated with parenchymal and stromal CD8 T-cell geographies. CSF1R and NECTIN2 gene expression was observed to correlate inversely with parenchymal localization and directly with stromal CD8+ T-cell abundance. Conclusions: GEP signatures can be identified that are concordant with various CD8+ T-cell localization patterns in melanoma and SCCHN, demonstrating that GEP-IA can be developed to identify the immune status of interest in the TME. The specific genes identified have potential to elucidate mechanisms of resistance and/or inform I-O targets that can be further evaluated in relation to clinical significance in future studies.
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