Evidence map›Paper›PMID 38593808›Full record

ReviewCell reports. Medicine2024

Harnessing artificial intelligence for prostate cancer management.

Lingxuan Zhu, Jiahua Pan, Weiming Mou, Longxin Deng, Yinjie Zhu, Yanqing Wang, Gyan Pareek, Elias Hyams, Benedito A Carneiro, Matthew J Hadfield and 11 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed.

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  15. Artificial intelligence in prostate cancer.Chinese medical journal · 2025
    Review
  16. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Lingxuan ZhuDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Changping Laboratory, Beijing, China.
Jiahua PanDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
Weiming MouDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Longxin DengDepartment of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
Yinjie ZhuDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
Yanqing WangDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
Gyan PareekDepartment of Surgery (Urology), Brown University Warren Alpert Medical School, Providence, RI, USA; Minimally Invasive Urology Institute, Providence, RI, USA.
Elias HyamsDepartment of Surgery (Urology), Brown University Warren Alpert Medical School, Providence, RI, USA; Minimally Invasive Urology Institute, Providence, RI, USA.
Benedito A CarneiroThe Legorreta Cancer Center at Brown University, Lifespan Cancer Institute, Providence, RI, USA.
Matthew J HadfieldThe Legorreta Cancer Center at Brown University, Lifespan Cancer Institute, Providence, RI, USA.
Wafik S El-DeiryThe Legorreta Cancer Center at Brown University, Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Department of Pathology & Laboratory Medicine, The Warren Alpert Medical School of Brown University, The Joint Program in Cancer Biology, Brown University and Lifespan Health System, Division of Hematology/Oncology, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
Tao YangDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Tao TanFaculty of Applied Sciences, Macao Polytechnic University, Address: R. de Luís Gonzaga Gomes, Macao, China.
Tong TongCollege of Physics and Information Engineering, Fuzhou University, Fujian 350108, China.
Na TaDepartment of Pathology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
Yan ZhuDepartment of Pathology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
Yisha GaoDepartment of Pathology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
Yancheng LaiDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Liang ChengDepartment of Surgery (Urology), Brown University Warren Alpert Medical School, Providence, RI, USA; Department of Pathology and Laboratory Medicine, Department of Surgery (Urology), Brown University Warren Alpert Medical School, Lifespan Health, and the Legorreta Cancer Center at Brown University, Providence, RI, USA. Electronic address: liang_cheng@yahoo.com.
Rui ChenDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China. Electronic address: drchenrui@foxmail.com.
Wei XueDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China. Electronic address: uroxuewei@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

Indexed as

Artificial IntelligenceProstatic NeoplasmsClinical Decision-MakingHumansMaleartificial intelligencemachine learningpathologyprostate cancerwhole-slide image

Identifiers

PMID38593808
PMCPMC11031422

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.