Development of a Visually Calculated SUV<sub>mean</sub>(HIT Score) on Screening PSMA PET/CT to Predict Treatment Response to<sup>177</sup>Lu-PSMA Therapy: Comparison with Quantitative SUV<sub>mean</sub>and Patient Outcomes
Bibliographic record
Abstract
<sup>177</sup>Lu-PSMA therapy is an effective treatment in patients with metastatic castration-resistant prostate cancer. SUV<sub>mean</sub> is a valuable screening biomarker to assess the suitability for <sup>177</sup>Lu-PSMA therapy but requires quantitative software. This study aims to develop a simple, clinically applicable prostate-specific membrane antigen PET/CT score that encompasses the elements of SUV<sub>mean</sub> without requiring additional quantification. <b>Methods:</b> Datasets from ethics-approved trials of patients with metastatic castration-resistant prostate cancer after androgen receptor signaling inhibition and taxane chemotherapy (or unfit for taxane), who were treated with <sup>177</sup>Lu-PSMA-617 and <sup>177</sup>Lu-PSMA I&T with a pretreatment screening with <sup>68</sup>Ga-PSMA-11 PET/CT, and clinical outcome data, including a prostate-specific antigen (PSA) 50% response rate (PSA50), PSA progression-free survival (PSA-PFS), and overall survival (OS), were included. The screening <sup>68</sup>Ga-PSMA-11 PET/CT of all participants was analyzed both semiquantitatively and visually. Semiquantitative analysis was used to derive the SUV<sub>mean</sub>. Visual analysis of the <sup>68</sup>Ga-PSMA-11 PET/CT images involved a binary visual heterogeneity assessment (homogeneous or heterogeneous), allocating a tumor SUV<sub>max</sub> range (<15, 15–29, 30–49, 50–79, or ≥80). A 4-category score incorporating both heterogeneity and intensity of tumors (HIT) was then developed as a combination of heterogeneity and intensity (SUV<sub>max</sub> range). The SUV<sub>max</sub> was less than 15 for score 1, 15–79 with heterogeneous intensity for score 2, 15–79 with homogeneous intensity for score 3, and 80 or greater for score 4. This score was evaluated according to clinical outcomes (PSA50, PSA-PFS, and OS) and compared with SUV<sub>mean</sub>. <b>Results:</b> Data from 139 participants were analyzed. In total, 75 (54%) patients achieved a PSA50 with a median PSA-PFS of 5.5 mo (95% CI, 4.1–6.0 mo) and an OS of 13.5 mo (95% CI, 11.1–17.9 mo). SUV<sub>mean</sub> was associated with PSA50 and survival outcomes when analyzed as a continuous variable or as quartiles. The PSA50 for HIT scores 1–4 was 0%, 39%, 65%, and 76%, respectively. The HIT score was strongly related to PSA-PFS and OS (log-rank test, <i>P</i> < 0.001 and <i>P</i> = 0.002). The median PSA-PFS for HIT scores 1–4 was 1.0, 4.1, 6.0, and 8.5, respectively, and the median OS was 7.6, 12.0, 18.5, and 16.9 mo, respectively. Cohen κ between readers for the HIT score was 0.71. <b>Conclusion:</b> A prostate-specific membrane antigen PET/CT score incorporating HIT derived from tools on a standard PET workstation is comparable with quantitative SUV<sub>mean</sub> as a prognostic tool following <sup>177</sup>Lu-PSMA therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".