NDLI: Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer

Content Provider	Springer Nature : BioMed Central
Author	Dadhania, Vipulkumar Gonzalez, Daniel Yousif, Mustafa Cheng, Jerome Morgan, Todd M. Spratt, Daniel E. Reichert, Zachery R. Mannan, Rahul Wang, Xiaoming Chinnaiyan, Anya Cao, Xuhong Dhanasekaran, Saravana M. Chinnaiyan, Arul M. Pantanowitz, Liron Mehra, Rohit
Abstract	Background TMPRSS2-ERG gene rearrangement, the most common E26 transformation specific (ETS) gene fusion within prostate cancer, is known to contribute to the pathogenesis of this disease and carries diagnostic annotations for prostate cancer patients clinically. The ERG rearrangement status in prostatic adenocarcinoma currently cannot be reliably identified from histologic features on H&E-stained slides alone and hence requires ancillary studies such as immunohistochemistry (IHC), fluorescent in situ hybridization (FISH) or next generation sequencing (NGS) for identification. Methods Objective We accordingly sought to develop a deep learning-based algorithm to identify ERG rearrangement status in prostatic adenocarcinoma based on digitized slides of H&E morphology alone. Design Setting, and Participants: Whole slide images from 392 in-house and TCGA cases were employed and annotated using QuPath. Image patches of 224 × 224 pixel were exported at 10 ×, 20 ×, and 40 × for input into a deep learning model based on MobileNetV2 convolutional neural network architecture pre-trained on ImageNet. A separate model was trained for each magnification. Training and test datasets consisted of 261 cases and 131 cases, respectively. The output of the model included a prediction of ERG-positive (ERG rearranged) or ERG-negative (ERG not rearranged) status for each input patch. Outcome measurements and statistical analysis: Various accuracy measurements including area under the curve (AUC) of the receiver operating characteristic (ROC) curves were used to evaluate the deep learning model. Results and Limitations All models showed similar ROC curves with AUC results ranging between 0.82 and 0.85. The sensitivity and specificity of these models were 75.0% and 83.1% (20 × model), respectively. Conclusions A deep learning-based model can successfully predict ERG rearrangement status in the majority of prostatic adenocarcinomas utilizing only H&E-stained digital slides. Such an artificial intelligence-based model can eliminate the need for using extra tumor tissue to perform ancillary studies in order to assess for ERG gene rearrangement in prostatic adenocarcinoma.
Related Links	https://bmccancer.biomedcentral.com/counter/pdf/10.1186/s12885-022-09559-4.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14712407
DOI	10.1186/s12885-022-09559-4
Journal	BMC Cancer
Issue Number	1
Volume Number	22
Language	English
Publisher	BioMed Central
Publisher Date	2022-05-05
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Surgical Oncology Health Promotion and Disease Prevention Biomedicine Medicine Public Health Prostate cancer ERG Deep learning Artificial intelligence Gene fusion Adenocarcinoma Whole slide imaging Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Cancer Research Oncology Genetics
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.8/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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