Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using <sup>18</sup>F-FDG PET/CT in Non–Small Cell Lung Cancer?
Bibliographic record
Abstract
UNLABELLED: Previously, we showed that a CT window and level setting of 1,600 and -300 Hounsfield units, respectively, and autocontouring using an (18)F-FDG PET 50% intensity level correlated best with pathologic results. The aim of this study was to compare this autocontouring with manual contouring, to determine which method is better. METHODS: Seventeen patients with non-small cell lung cancer underwent (18)F-FDG PET/CT before surgery. The maximum diameter on pathologic examination was determined. Seven sets of gross tumor volumes (GTVs) were defined. The first set (GTV(CT)) was contoured manually using only CT information. The second set (GTV(Auto)) was autocontoured using a 50% intensity level for (18)F-FDG PET images. The third set (GTV(Manual)) was manually contoured using a visual method on PET images. The other 4 sets combined CT and (18)F-FDG PET images fused to one another to become composite volumes: GTV(CT+Auto), GTV(CT+Manual), GTV(CT-Auto), and GTV(CT-Manual). To quantitate the degree to which CT and (18)F-FDG PET defined the same region of interest, a matching index was calculated for each case. The maximum diameter of GTV was compared with the maximum diameter on pathologic examination. RESULTS: The median GTV(CT), GTV(Auto), GTV(Manual), GTV(CT+Auto), GTV(CT+Manual), GTV(CT-Auto), and GTV(CT-Manual) were 6.96, 2.42, 4.37, 7.46, 10.17, 2.21, and 3.38 cm(3), respectively. The median matching indexes of GTV(CT) versus GTV(CT+Auto), GTV(Auto) versus GTV(CT+Auto), GTV(CT) versus GTV(CT+Manual), and GTV(Manual) versus GTV(CT+Manual) were 0.86, 0.65, 0.88, and 0.81, respectively. Compared with the maximum diameter on pathologic examination, the correlations of GTV(CT), GTV(Auto), GTV(Manual), GTV(CT+Auto), and GTV(CT+Manual) were 0.87, 0.83, 0.93, 0.86, and 0.94, respectively. CONCLUSION: The matching index was higher for manual contouring than for autocontouring using a 50% intensity level on (18)F-FDG PET images. When using a 50% intensity level to contour the target of non-small cell lung cancer, one should also consider using manual contouring of (18)F-FDG PET to check for any missed disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".