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Record W4404390173 · doi:10.1097/cm9.0000000000003328

Commentary on Preliminary clinical practice of radical prostatectomy without preoperative biopsy

2024· article· en· W4404390173 on OpenAlexaff
John D. Denstedt

Bibliographic record

VenueChinese Medical Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsWestern University
Fundersnot available
KeywordsProstatectomyMedicineBiopsyUrologyGeneral surgeryProstate biopsyProstate cancerRadiologyInternal medicineCancer

Abstract

fetched live from OpenAlex

Since Xing et al[1] first proposed radical prostatectomy (RP) without preoperative biopsy for patients with highly suspicious prostate cancer identified by multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen (PSMA) positron emission tomography (PET) in 2019, several original articles have reported attempts in this direction by various independent teams. Despite sparking widespread debate, these articles and discussions have provided new insights into the diagnostic and treatment paradigms for prostate cancer. Recently, Xing’s team updated their result in the Chinese Medical Journal. A series of 56 patients with standardized uptake value (SUVmax) of ≥4 and Prostate Imaging Reporting and Data System (PI-RADS) score of ≥4 lesions underwent RP without prior biopsy. Postoperative pathology revealed that one patient (1.8%) was diagnosed with benign disease (high-grade prostatic intraepithelial neoplasia), while six patients (10.7%) were confirmed to have clinically insignificant prostate cancer (International Society of Urological Pathology [ISUP] grade <2). These false-positive results underscore the importance of careful patient selection within this biopsy-free pathway. According to the authors’ protocol, evaluating the cut-off of SUVmax to 7.5 could eliminate these false-positive results. It is essential to note that a risk stratification system for prostate cancer based on mpMRI and PSMA PET is currently lacking, leaving the optimal cut-off values for PI-RADS score and SUVmax that accurately diagnose clinically significant prostate cancer without false positives unclear. Although preoperative biopsy remains the current standard process prior to RP and is considered essential in the urologic community, clinical practice has shown that for several malignancies, such as renal cell carcinoma and lung cancer, preoperative biopsy is not routinely necessary and treatment typically proceeds based on imaging. The commonality among these tumors lies in the sufficiently high accuracy of imaging-based diagnosis. The development of next-generation imaging may pave the way for omitting unnecessary biopsies in prostate cancer, at least for some patients. Furthermore, the recent reports on RP without preoperative biopsy may also reflect the genuine needs of certain patients. Fear of potential malignancies drives a small subset of patients to accept the risk of no cancer after surgery rather than spend several weeks in the traditional biopsy driven process along with the well-known associated potential adverse events of prostate biopsy. It is important to acknowledge the limitations in the current study including the retrospective nature, small numbers of patients all of whom had significant preexisting erectile dysfunction and a relatively high median preoperative prostate specific antigen (PSA) level of 21. Nonetheless for appropriately selected patients, this approach could be a new paradigm for prostate cancer diagnosis and treatment. In conclusion, reports such as this have raised important questions regarding prostate cancer diagnosis. Currently, prostate biopsy remains a necessary step before RP. Larger prospective clinical trials to define strict inclusion criteria are required to provide the necessary evidence in support of an imaging-based approach to prostate cancer diagnosis. Conflicts of interest None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.393
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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