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Combination of Deep and Statistical Features of the Tissue of Pathology Images to Classify and Diagnose the Degree of Malignancy of Prostate Cancer

Published Online: 2024-12-11

Authors

Gao, Yan

Vali, Mahsa
License Information

Text and Data Mining valid from 2024-12-11

Version of Record valid from 2024-12-11
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Article History

Received: 9 October 2024

Revised: 19 November 2024

Accepted: 29 November 2024

First Online: 11 December 2024

Declarations

:

: The ProstateX dataset has been included in the PI-CAI Public Training and Development dataset. As such, ProstateX as a benchmark has been deprecated and is superseded by the PI-CAI challenge. PI-CAI is an all-new grand challenge, with over 10,000 carefully curated prostate MRI exams to validate modern AI algorithms and estimate radiologists’ performance at clinically significant prostate cancer detection and diagnosis. Key aspects of the study design have been established in conjunction with an international, multi-disciplinary scientific advisory board (16 experts in prostate AI, radiology, and urology)—to unify and standardize present-day guidelines, and to ensure meaningful validation of prostate-AI towards clinical translation.

: This work does not involve the use of human subjects. And only the PROSTATEx dataset was used. Therefore, there is no need to obtain permission from the participants.

: Human subjects were not used in this research. Therefore, there is no need to obtain consent for publication.

: The authors declare no competing interests.

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