Is segmentation performance of deep-learning models affected by cancer type? A performance analysis on PET/CT
Bibliographic record
Abstract
In cancer care, positron emission tomography/computed tomography (PET/CT) is used across a variety of cancer types. Though automatic tumor segmentation in PET/CT is improving with advanced deep learning (DL), their performance across different cancer types is under-explored. Therefore, we evaluated two state-of-the-art DL models using a publicly available PET/CT dataset that includes three cancer types: lung cancer, lymphoma, and melanoma, along with normal controls (1014 studies overall). This dataset’s uniform image acquisition protocol (including scanner) mitigates domainshift and allows for a reliable evaluation. The models assessed, nnU-Net and Segment Anything Model (SAM), were prompted with bounding-boxes based on nnU-Net segmentation (nnUNet-SAM). The evaluations were performed using five-fold cross-validation investigating: (a) patient-based analysis (all tumor sites together) and (b) lesion-based analysis (isolated tumor sites) using the Dice coefficient (DC) as the evaluation metric. The Kruskal-Wallis H test and test of proportions were applied to compare performance among the cancer types. The patient-based analysis revealed variations among lung cancer (N = 168), lymphoma (N = 145), and melanoma (N = 188) using average DC (0.73, 0.73, 0.63, p = 0.002, using nnU-net which performed better). The lesion-based analysis indicated significant variation (p < 10-5 ) among the cancer types using average lesion-based DC. This variation was lower considering the highest lesion-based DC. The largest lesion contributed more frequently to the highest lesion-based DC in lung cancer (71% – 90%) compared to lymphoma (63% – 88%) and melanoma (57% – 76%). The findings underscore the need for a personalized tumor segmentation approach and suggest taking caution for automated tumor analysis across cancer types using PET/CT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".