Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer

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Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer. / Nimgaonkar, Vivek; Krishna, Viswesh; Krishna, Vrishab; Tiu, Ekin; Joshi, Anirudh; Vrabac, Damir; Bhambhvani, Hriday; Smith, Katelyn; Johansen, Julia S.; Makawita, Shalini; Musher, Benjamin; Mehta, Arnav; Hendifar, Andrew; Wainberg, Zev; Sohal, Davendra; Fountzilas, Christos; Singhi, Aatur; Rajpurkar, Pranav; Collisson, Eric A.

I: Cell Reports Medicine, Bind 4, Nr. 4, 101013, 2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Nimgaonkar, V, Krishna, V, Krishna, V, Tiu, E, Joshi, A, Vrabac, D, Bhambhvani, H, Smith, K, Johansen, JS, Makawita, S, Musher, B, Mehta, A, Hendifar, A, Wainberg, Z, Sohal, D, Fountzilas, C, Singhi, A, Rajpurkar, P & Collisson, EA 2023, 'Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer', Cell Reports Medicine, bind 4, nr. 4, 101013. https://doi.org/10.1016/j.xcrm.2023.101013

APA

Nimgaonkar, V., Krishna, V., Krishna, V., Tiu, E., Joshi, A., Vrabac, D., Bhambhvani, H., Smith, K., Johansen, J. S., Makawita, S., Musher, B., Mehta, A., Hendifar, A., Wainberg, Z., Sohal, D., Fountzilas, C., Singhi, A., Rajpurkar, P., & Collisson, E. A. (2023). Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer. Cell Reports Medicine, 4(4), [101013]. https://doi.org/10.1016/j.xcrm.2023.101013

Vancouver

Nimgaonkar V, Krishna V, Krishna V, Tiu E, Joshi A, Vrabac D o.a. Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer. Cell Reports Medicine. 2023;4(4). 101013. https://doi.org/10.1016/j.xcrm.2023.101013

Author

Nimgaonkar, Vivek ; Krishna, Viswesh ; Krishna, Vrishab ; Tiu, Ekin ; Joshi, Anirudh ; Vrabac, Damir ; Bhambhvani, Hriday ; Smith, Katelyn ; Johansen, Julia S. ; Makawita, Shalini ; Musher, Benjamin ; Mehta, Arnav ; Hendifar, Andrew ; Wainberg, Zev ; Sohal, Davendra ; Fountzilas, Christos ; Singhi, Aatur ; Rajpurkar, Pranav ; Collisson, Eric A. / Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer. I: Cell Reports Medicine. 2023 ; Bind 4, Nr. 4.

Bibtex

@article{b6fd997c558b46369b081865634f3513,
title = "Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer",
abstract = "Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n = 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n = 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n = 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist.",
keywords = "digital pathology, pancreatic cancer, predictive biomarker",
author = "Vivek Nimgaonkar and Viswesh Krishna and Vrishab Krishna and Ekin Tiu and Anirudh Joshi and Damir Vrabac and Hriday Bhambhvani and Katelyn Smith and Johansen, {Julia S.} and Shalini Makawita and Benjamin Musher and Arnav Mehta and Andrew Hendifar and Zev Wainberg and Davendra Sohal and Christos Fountzilas and Aatur Singhi and Pranav Rajpurkar and Collisson, {Eric A.}",
note = "Publisher Copyright: {\textcopyright} 2023 Valar Labs, Inc.",
year = "2023",
doi = "10.1016/j.xcrm.2023.101013",
language = "English",
volume = "4",
journal = "Cell Reports Medicine",
issn = "2666-3791",
publisher = "Cell Press",
number = "4",

}

RIS

TY - JOUR

T1 - Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer

AU - Nimgaonkar, Vivek

AU - Krishna, Viswesh

AU - Krishna, Vrishab

AU - Tiu, Ekin

AU - Joshi, Anirudh

AU - Vrabac, Damir

AU - Bhambhvani, Hriday

AU - Smith, Katelyn

AU - Johansen, Julia S.

AU - Makawita, Shalini

AU - Musher, Benjamin

AU - Mehta, Arnav

AU - Hendifar, Andrew

AU - Wainberg, Zev

AU - Sohal, Davendra

AU - Fountzilas, Christos

AU - Singhi, Aatur

AU - Rajpurkar, Pranav

AU - Collisson, Eric A.

N1 - Publisher Copyright: © 2023 Valar Labs, Inc.

PY - 2023

Y1 - 2023

N2 - Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n = 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n = 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n = 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist.

AB - Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n = 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n = 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n = 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist.

KW - digital pathology

KW - pancreatic cancer

KW - predictive biomarker

U2 - 10.1016/j.xcrm.2023.101013

DO - 10.1016/j.xcrm.2023.101013

M3 - Journal article

C2 - 37044094

AN - SCOPUS:85152254090

VL - 4

JO - Cell Reports Medicine

JF - Cell Reports Medicine

SN - 2666-3791

IS - 4

M1 - 101013

ER -

ID: 362891544