Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features

We developed and validated a robust machine learning model incorporating intratumoral and peritumoral radiomic features, along with clinicoradiological and metabolic parameters, to accurately identify csPCa. The prediction process was visualized via SHAP analysis to facilitate clinical decision- making.

Authors

Dan Song (ORCID: https://orcid.org/0000-0002-5544-9820)
Min Hou (ORCID: https://orcid.org/0000-0002-0524-6806)
Wenjun Zhao (ORCID: https://orcid.org/0000-0002-6420-9888)
Yongchao Niu (ORCID: https://orcid.org/0000-0002-9774-5417)
Juan Wang (ORCID: https://orcid.org/0000-0001-9103-3315)

Institutions

Xinxiang Central Hospital (CN)
Henan Medical University (CN)

Publication Details

Journal: BMC Medical Imaging
Published: 2024-12-30
DOI: https://doi.org/10.1186/s12880-024-01548-2
Citations: 9
Primary Topic: Prostate Cancer Diagnosis and Treatment
Type: article
Field-Weighted Citation Impact: 3.77

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features

Dan Song, Min Hou, Wenjun Zhao, Yongchao Niu et al.

Dec 30, 2024

9 citations

BMC Medical Imaging

Prostate Cancer Diagnosis and Treatment

3.77

article

Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features

Dan Song, Min Hou, Wenjun Zhao, Yongchao Niu, Juan Wang

December 30, 2024article en

9 citations

Abstract

We developed and validated a robust machine learning model incorporating intratumoral and peritumoral radiomic features, along with clinicoradiological and metabolic parameters, to accurately identify csPCa. The prediction process was visualized via SHAP analysis to facilitate clinical decision- making.

BMC Medical ImagingVol. 24(1)

Xinxiang Central Hospital (CN), Henan Medical University (CN)

Openalex Percentile: Top 6%

Prostate Cancer Diagnosis and Treatment

3.77

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features — Dan Song, Min Hou, et al. · BMC Medical Imaging (2024) | TGRS Research Map | TGRS