Whole-body low-dose CT can be of value in prostate cancer decision-making: a retrospective study on 601 patients
Bibliographic record
Abstract
OBJECTIVES: To evaluate the diagnostic value of whole-body low-dose computed tomography (CT) to detect bone metastasis in prostate cancer (PCa) patients and its possible utility in therapeutic decision-making. Also, to determine the valuable CT features for lesion characterisation. METHODS: Ga-PSMA-PET results. The per-lesion and per-patient diagnostic performances were calculated. Also, the significance of CT features was evaluated. Moreover, the inter-observer agreement was analysed. A two-tailed p value < 0.05 was considered significant. RESULTS: From 727 reviewed PCa patients, 601 (mean age = 68.7 ± 8.1) were found to be eligible, including 211 (35.1%) referrals for initial staging and 390 (64.9%) for evaluating the extent of the disease after biochemical recurrence. Per-patient diagnostic analysis for three reviewers showed 81.0-89.4% sensitivity and 96.6-98.5% specificity in detecting osteo-metastasis. It was able to correctly detect high-burden disease based on both CHAARTED and LATITUDE criteria. Regarding the value of underlying CT features, size > 1 cm, ill-defined borders, presence of soft-tissue component, and cortical destruction were statistically in favour of metastasis. Also, Hu > 900 was in favour of benign entities with 93% specificity. CONCLUSIONS: Ga-PSMA PET/CT, whole-body low-dose CT might precisely classify PCa patients considering therapeutic decision-making. Additionally, we proposed diagnostic CT features that could help radiologists with better characterisation of the detected lesions. CRITICAL RELEVANCE STATEMENT: The whole-body low-dose CT can be considered valuable in the clinical decision-making of prostate cancer patients. This modality may obviate performing multiple imaging sessions and high-cost scans in patients diagnosed with the high-burden disease.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".