NCT07727122Toborzás még nem indult
Multicenter Prospective Validation of AI Models for Malignancy Risk Prediction in Pulmonary Nodules
A Multicenter Prospective Diagnostic Accuracy Study of Three CT-Based Artificial Intelligence Models for Predicting Malignancy Risk in Pulmonary Nodules Using Pathology as the Gold Standard
Forrás: ClinicalTrials.gov · Utoljára frissítve: 2026. júl. 27.
Rövid összefoglaló (regiszter, angolul)
This multicenter prospective diagnostic accuracy study will compare the performance of three artificial intelligence (AI) models (MVCS, LungDoc, and a United Imaging AI model) for predicting the malignancy risk of pulmonary nodules on chest CT. All enrolled patients will have pulmonary nodules ≤3 cm on CT and a definitive postoperative or biopsy pathological diagnosis. The AI models will generate continuous malignancy probability scores based only on CT images. Pathology will serve as the gold standard. The primary objective is to compare the area under the receiver operating characteristic curve (AUC) for malignancy prediction among the three AI models. Secondary objectives include comparison of sensitivity, specificity, positive and negative predictive values, accuracy, F1 score, and calibration. Exploratory analyses will evaluate the MVCS model for predicting pathological invasion degree (pre-invasive, minimally invasive, and invasive adenocarcinoma) and an extended MVCSN model that incorporates clinical and imaging features in a data-complete subset.
- Állapot (regiszter)
- Toborzás még nem indult
- Vizsgálattípus
- Megfigyeléses
- Résztvevők száma
- 3000 (tervezett)
- Első benyújtás
- 2026. júl. 18.
- Kezdés
- 2026. júl.
- Elsődleges befejezés
- 2028. márc.
- Fő szponzor
- Guangdong Provincial People's Hospital
- NCT-szám
- NCT07727122
Mit vállalsz
- Vizsgálat típusa: Megfigyeléses
- Tervezett résztvevők: 3000
- 2026. júl. és az elsődleges befejezés (2028. márc.) között
- 5 helyszín, 1 országban
- Vizitek és vizsgálatok: kérdezd a vizsgálat csapatát, a regiszter nem sorolja fel őket
A(z) ClinicalTrials.gov regisztermezőiből összeállítva. A vizsgálat felépítését írja le.
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Korábbi kezelés
- The patient receives treatments between CT and pathology that may significantly alter nodule appearance (e.g., chemotherapy, radiotherapy, targeted therapy).
Más betegség
- Required metadata for any AI model are missing and cannot be imputed. History of other malignant tumors (malignancies other than the index non-small cell lung cancer).
- Severe psychiatric illness, cognitive impairment, or other conditions that prevent cooperation with study-related procedures and follow-up.
- Participation in another clinical study that may interfere with the results of this research.
- -The patient or legal representative refuses participation.
- Exclusion (Post-Enrollment / Removal from Analysis)
- Participants already enrolled may be excluded from the analysis set if:
- Required AI model assessments are not completed (e.g., technical failure to generate outputs).
Egyéb
- Pathological results are unclear, inconclusive, or disputed; nodule nature or grade cannot be reliably determined.
- CT imaging data are incomplete (missing essential series) or have severe motion, metal, or other artifacts preventing accurate AI analysis.
- They are later found not to meet inclusion criteria or to meet exclusion criteria.
- No usable data are available after enrollment.
- Critical data are missing, preventing contribution to primary analysis.
- The interval between CT and pathology exceeds 6 months.
Kiket keres a vizsgálat
- Age ≥ 18 years, any sex.
- At least one pulmonary nodule detected on chest CT, with initial nodule diameter ≤ 3 cm.
- The nodule undergoes surgical resection or biopsy with a definitive benign or malignant pathological diagnosis.
- Time interval between CT examination and pathological examination ≤ 6 months.
- Availability of complete CT imaging data in DICOM format with adequate image quality (no severe artifacts), meeting input requirements of all three AI models.
- Availability of complete clinicopathologic information including histologic type and grade, with clear pathological diagnosis suitable as gold standard labels for AI validation.
- -The patient (or legally authorized representative) is willing and able to sign written informed consent.
Életkor a regiszterben: 18 év – - · Nem: nők és férfiak · Egészséges önkénteseket nem fogad
Helyszínek (5)
Helyszínek a regiszter sorrendjében. Ha a profilodba mented a városodat, távolság szerint rendeződnek.
| Helyszín | Város | Ország | Helyszín állapota |
|---|---|---|---|
| Second Affiliated Hospital of Army Medical University (Xinqiao Hospital) | Chongqing | Kína | Ismeretlen állapot |
| Zhujiang Hospital, Southern Medical University | Guangzhou | Kína | Ismeretlen állapot |
| Guangdong Provincial People's Hospital | Guangzhou | Kína | Ismeretlen állapot |
| Zhejiang University | Hangzhou | Kína | Ismeretlen állapot |
| Affiliated Hospital of Xuzhou Medical University | Xuzhou | Kína | Ismeretlen állapot |
Ez az oldal tájékoztatás, nem orvosi tanács. Bármely vizsgálatról az orvosoddal beszélj.