Bibliographic record
Abstract
目的:探讨18F-FDG PET、CT和血管造影对原发性肝癌TACE后残留及转移病灶的检出能力.材料与方法:80例经穿刺活检或手术病理证实的肝癌患者,其中高分化肝细胞癌20例、中分化肝细胞癌44例、低分化肝细胞癌11例、肝胆管细胞癌3例、肝腺癌2例.以临床随访6个月以上及部分病理结果为标准,回顾性分析TACE后1.5～2个月18F-FDG PET、CT和血管造影对肿瘤残留及转移病灶的显示情况.结果:80例患者肝内共104个病灶,经临床随访6个月以上及部分病理结果证实,有肿瘤残留病灶62个,PET正确检出56个,CT正确检出38个,血管造影正确检出58个;无肿瘤残留病灶42个,PET正确检出40个,CT正确检出40个,血管造影正确检出42个.PET和血管造影对肝癌TACE后肿瘤残留病灶检出的灵敏度和准确性分别为90.3%、92.3%和93.6%、96.2%,明显高于CT(61.3%、75.0%),差异显著(P＜0.01).同时PET检出了CT和血管造影无法发现的肝外转移病灶5例.结论:CT是肝癌TACE后最常用的随访方法,可以清晰显示碘油在病灶内的分布情况.18F-FDG PET显像能够更加准确的鉴别肿瘤存活,特别是CT无法明确的病变,而且对于肝外转移病灶的检出具有独特的优势.血管造影的诊断灵敏度、准确性最高,但是属于有创性检查.将多种影像学方法相结合,能够为临床提供更加可靠的定位和定性的诊断依据.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".