ArticleCancer cell international2025
An online clustering algorithm predicting model for prostate cancer based on PHI-related variables and PI-RADS in different PSA populations.
Article in Cancer cell international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
- Beyond AUC: a clinician's guide to building and trusting prediction models in oncology-a narrative review.Frontiers in oncology · 2026Review
- Using Machine Learning to Create Prognostic Systems for Primary Prostate Cancer.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
BACKGROUND AND
aimProstate cancer is the most common male malignancy. Current diagnostic methods using single TPSA and PHI lack specificity. Some researches have created nomograms for predicting risk, but these are not easily visualized. Our study aims to find the best negative predictive value (NPV) for PHI, then build a clustering model to display prostate cancer risk categories, particularly useful for patients with PSA > 20 and be actually applied in clinical work.
methodWe collected 708 patients in the training cohort and 143 in the validation cohort, divided into three groups based on their PSA levels. Next, we determined optimal and customized PHI cut-off values, calculated NPV and PPV, and selected logistic regression as the best method among several machine-learning algorithms. Subsequently, the significant variables were identified, and then a clustering algorithm was constructed. Finally, the model was validated and made available online for further clinical application.
resultsThe Optimal PHI cut-off lower limits for PSA > 4, PSA4-20, PSA > 20 subgroups were 23.85, 24.35, and 40.75, with upper limits of 142.9, 143, and 135.6, respectively. The clustering model of the optimal cohort for PSA > 4 and PSA 4-20 sub-groups showed a superior Silhouette coefficients of 0.433 and 0.526 than that of the customized PHI cohort (0.432, 0.452). The PSA > 20 subgroup owned the highest Silhouette coefficient of 0.572. The validation cohort showed AUC values of 0.761, 0.823, 0.833 for these 3 sub-groups, with accuracy rates of 88.81%, 90.38%, and 82.05%.
conclusionIn conclusion, our clustering model effectively categorizes patients into distinct risk groups with clear visualization and has demonstrated stability and reliability in the validation cohort, potentially aiding in early diagnosis of prostate cancer in clinical practice.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.