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Diagnostics
Volume 16
Issue 2
10.3390/diagnostics16020356
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Clinically Translatable Multimodal Deep Learning Model for HRD Detection from Histopathology Images

by
Mohan Uttarwar
1,2,3,*,
Jayant Khandare
1,
P. M. Shivamurthy
2,
Adithya Satpute
2,
Mohith Panwar
2,
Hrishita Kothavade
2,
Aarthi Ramesh
2,
Sandhya Iyer
2 and
Gowhar Shafi
1,3,*
1
School of Consciousness, Dr. Vishwanath Karad MIT World Peace University, Kothrud, Pune 411038, India
2
1Cell.Ai, 209 B, GO Square, Aundh-Hinjewadi Road Wakad, Pune 411057, India
3
1Cell.Ai, 320, Hutch Dr, Foster City, CA 94404, USA
*
Authors to whom correspondence should be addressed.
Diagnostics 2026, 16(2), 356; https://doi.org/10.3390/diagnostics16020356
Submission received: 11 December 2025 / Revised: 8 January 2026 / Accepted: 16 January 2026 / Published: 21 January 2026
(This article belongs to the Special Issue Recent Advances in Pathology 2025)
Versions Notes

Abstract

Background: With extensive research and development in the past decade, the affordability of Poly (ADP-ribose) polymerase (PARP) inhibitor therapy has drastically improved. Homologous recombination deficiency (HRD), a key biomarker, has been identified as an important guiding factor for PARP inhibitor therapeutic decisions in breast and ovarian cancer. However, identification of patients who will respond to Poly (ADP-ribose) polymerase (PARP) inhibitor therapy is challenging due to the lack of a unifying morphological phenotype. Current HRD testing via next-generation sequencing (NGS) is tissue-dependent, has high failure rates, misses relevant HRD genes, and involves longer turn-around times. Methods: To overcome these limitations, we developed a multimodal AI model, TRINITY, combining imaging, image-based transcriptome data, and clinico-molecular data, to examine whole-slide images (WSIs) obtained from hematoxylin and eosin (H&E)-stained samples to non-invasively predict HRD status. Results: The TRINITY model, tested on 316 TCGA breast and OV samples, presented a sensitivity of 0.77 and 0.91, NPV of 0.94 and 0.86, PPV of 0.63 and 0.58, specificity of 0.89 and 0.47, and AUC-ROC of 0.91 and 0.72, respectively. The model also yielded a similar outcome in a blind study of 74 samples, with a sensitivity of 81.2, NPV of 0.85, PPV of 0.77, specificity of 0.81, and high AUC-ROC value of 0.89, showing its promising preliminary evidence of predicting HRD status on external cohorts. Conclusions: These findings demonstrate TRINITY’s potential as a rapid, cost-effective, and tissue-sparing alternative to conventional NGS testing. While promising, further validation is needed to establish its generalizability across broader cancer types.
Keywords: artificial intelligence; HRD; transformers; embeddings; TRINITY; transcriptomics; clinico-molecular; PARPi artificial intelligence; HRD; transformers; embeddings; TRINITY; transcriptomics; clinico-molecular; PARPi

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MDPI and ACS Style

Uttarwar, M.; Khandare, J.; Shivamurthy, P.M.; Satpute, A.; Panwar, M.; Kothavade, H.; Ramesh, A.; Iyer, S.; Shafi, G. A Clinically Translatable Multimodal Deep Learning Model for HRD Detection from Histopathology Images. Diagnostics 2026, 16, 356. https://doi.org/10.3390/diagnostics16020356

AMA Style

Uttarwar M, Khandare J, Shivamurthy PM, Satpute A, Panwar M, Kothavade H, Ramesh A, Iyer S, Shafi G. A Clinically Translatable Multimodal Deep Learning Model for HRD Detection from Histopathology Images. Diagnostics. 2026; 16(2):356. https://doi.org/10.3390/diagnostics16020356

Chicago/Turabian Style

Uttarwar, Mohan, Jayant Khandare, P. M. Shivamurthy, Adithya Satpute, Mohith Panwar, Hrishita Kothavade, Aarthi Ramesh, Sandhya Iyer, and Gowhar Shafi. 2026. "A Clinically Translatable Multimodal Deep Learning Model for HRD Detection from Histopathology Images" Diagnostics 16, no. 2: 356. https://doi.org/10.3390/diagnostics16020356

APA Style

Uttarwar, M., Khandare, J., Shivamurthy, P. M., Satpute, A., Panwar, M., Kothavade, H., Ramesh, A., Iyer, S., & Shafi, G. (2026). A Clinically Translatable Multimodal Deep Learning Model for HRD Detection from Histopathology Images. Diagnostics, 16(2), 356. https://doi.org/10.3390/diagnostics16020356

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