이 페이지는 자동 번역되었으며 번역의 정확성을 보장하지 않습니다. 참조하십시오 영문판 원본 텍스트의 경우.

Multicenter Prospective Validation of AI Models for Malignancy Risk Prediction in Pulmonary Nodules

2026년 7월 22일 업데이트: Wen-zhao ZHONG, Guangdong Provincial People's Hospital

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

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.

연구 개요

상태

아직 모집하지 않음

정황

상세 설명

Lung cancer is the leading cause of cancer-related morbidity and mortality worldwide. Low-dose CT (LDCT) screening improves early detection but generates a high prevalence of indeterminate pulmonary nodules and substantial false positives, leading to unnecessary invasive procedures and anxiety while still risking missed early cancers. Traditional radiologic assessment of pulmonary nodules relies on visual features and clinical risk factors, with limited performance and substantial reader variability, especially for small or subsolid nodules.

Recent advances in deep learning enable AI models to extract high-dimensional imaging features from CT scans and to predict nodule malignancy and risk stratification. Several commercial AI systems for pulmonary nodule assessment have been approved and deployed in clinical practice, and academic groups have proposed novel algorithms such as the multi-view coupled self-attention (MVCS) model. However, most prior studies have been single-center, retrospective, and used reference standards such as imaging follow-up or expert reading rather than biopsy pathology. Head-to-head comparisons of different AI models in prospective, multicenter real-world populations with pathological gold standard are lacking.

This study is a multicenter, prospective, diagnostic accuracy comparison of three purely imaging-based AI models-MVCS, LungDoc (Shukun Technology), and a United Imaging AI model-for predicting the malignancy of pulmonary nodules. Eligible patients are adults (≥18 years) with at least one pulmonary nodule ≤3 cm on CT, who undergo surgical or biopsy pathology with a definitive benign or malignant diagnosis, and with a CT-pathology interval ≤6 months. CT images in DICOM format will be collected using standardized acquisition parameters across centers and processed by the three AI models, which output continuous malignancy probabilities or suspicion scores. Investigators will be blinded to AI outputs.

The primary endpoint is the AUC for malignancy prediction for each model, and pairwise AUC comparisons using DeLong's test. Secondary endpoints include binary performance metrics (sensitivity, specificity, PPV, NPV, accuracy, F1 score) at model-native thresholds and optimal Youden index thresholds, as well as calibration (calibration curves, intercept, slope). Prespecified subgroup analyses will examine performance by age, sex, smoking status, nodule size, morphology, and study center, and random-effects methods will be used to assess center effects.

An exploratory aim will validate the MVCS model for predicting pathological invasion degree by classifying nodules into pre-invasive lesions (atypical adenomatous hyperplasia [AAH] / adenocarcinoma in situ [AIS]), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (ADC), using metrics such as multi-class accuracy, weighted F1 score, confusion matrix, Matthews correlation coefficient, and AUC for predefined binary sub-tasks. Another exploratory analysis will evaluate the MVCSN model, which incorporates CT images plus clinical and radiologic features, in a subset with complete data.

The study plans to enroll 3,000 pathologically confirmed pulmonary nodules across five centers in China over approximately 30 months. No experimental treatment is administered; all clinical management, imaging, and pathology follow standard of care. Risks are limited to those associated with clinically indicated pathology procedures (e.g., surgery or biopsy).

연구 유형

관찰

등록 (추정된)

3000

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

연구 장소

  • 중국
    • Chongqing Municipality
      • Chongqing, Chongqing Municipality, 중국, 400037
        • Second Affiliated Hospital of Army Medical University (Xinqiao Hospital)
        • 연락하다:
    • Guangdong
      • Guangzhou, Guangdong, 중국, 510000
        • Guangdong Provincial People's Hospital
        • 연락하다:
        • 수석 연구원:
          • Wenzhao Zhong, PhD
      • Guangzhou, Guangdong, 중국, 510000
        • Zhujiang Hospital, Southern Medical University
        • 연락하다:
    • Jiangsu
      • Xuzhou, Jiangsu, 중국, 221006
        • Affiliated Hospital of Xuzhou Medical University
        • 연락하다:
    • Zhejiang
      • Hangzhou, Zhejiang, 중국, 310003
        • Zhejiang University
        • 연락하다:

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

아니

샘플링 방법

비확률 샘플

연구 인구

Adults (≥18 years) undergoing routine clinical care at five tertiary hospitals in China who have at least one pulmonary nodule (≤3 cm) detected on chest CT and receive surgical or biopsy pathology with a definitive benign or malignant diagnosis. All participants have adequate CT image quality and complete clinicopathologic information for AI model validation

설명

Inclusion Criteria:

  • 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.

Exclusion Criteria:

  • Pathological results are unclear, inconclusive, or disputed; nodule nature or grade cannot be reliably determined.
  • The patient receives treatments between CT and pathology that may significantly alter nodule appearance (e.g., chemotherapy, radiotherapy, targeted therapy).
  • CT imaging data are incomplete (missing essential series) or have severe motion, metal, or other artifacts preventing accurate AI analysis.
  • 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:

  • They are later found not to meet inclusion criteria or to meet exclusion criteria.
  • No usable data are available after enrollment.
  • Required AI model assessments are not completed (e.g., technical failure to generate outputs).
  • Critical data are missing, preventing contribution to primary analysis.
  • The interval between CT and pathology exceeds 6 months.

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

코호트 및 개입

그룹/코호트
Single group
This study has only one group.

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
Area Under the ROC Curve (AUC) for Malignancy Prediction
기간: At the time of availability of pathology results, up to 6 months after index chest CT
For each pure imaging AI model (MVCS, LungDoc, United Imaging model), the AUC of the receiver operating characteristic curve for predicting malignant versus benign pulmonary nodules, based on continuous malignancy probabilities or suspicion scores. AUCs will be reported with 95% confidence intervals, and pairwise comparisons will be conducted using DeLong's test.
At the time of availability of pathology results, up to 6 months after index chest CT

2차 결과 측정

결과 측정
측정값 설명
기간
Sensitivity and Specificity for Malignancy Prediction
기간: At the time of availability of pathology results, up to 6 months after index chest CT
Sensitivity and specificity for classifying nodules as malignant vs benign for each AI model, using both (a) model-predefined thresholds and (b) optimal cut-off points determined by maximizing the Youden index. 95% confidence intervals will be reported; paired comparisons will use McNemar's test.
At the time of availability of pathology results, up to 6 months after index chest CT
Positive Predictive Value (PPV) and Negative Predictive Value (NPV)
기간: At the time of availability of pathology results, up to 6 months after index chest CT
PPV and NPV for malignancy prediction for each AI model at the same thresholds as above, with 95% confidence intervals.
At the time of availability of pathology results, up to 6 months after index chest CT
Overall Diagnostic Accuracy and F1 Score
기간: At the time of availability of pathology results, up to 6 months after index chest CT
Proportion of correctly classified nodules (accuracy) and F1 score for each AI model in the binary task of malignant versus benign nodules, with 95% confidence intervals.
At the time of availability of pathology results, up to 6 months after index chest CT
Calibration Metrics
기간: At the time of availability of pathology results, up to 6 months after index chest CT
Calibration performance of each AI model will be evaluated by calibration plots, calibration intercept, and calibration slope for predicted malignancy probability versus observed malignant proportion. Hosmer-Lemeshow goodness-of-fit test will be reported.
At the time of availability of pathology results, up to 6 months after index chest CT

기타 결과 측정

결과 측정
측정값 설명
기간
Multi-Class Accuracy of MVCS for Pathological Invasion Degree
기간: At the time of availability of pathology results, up to 6 months after index chest CT
For nodules with specific pathological subtypes (AAH, AIS, MIA, ADC), performance of the MVCS model in three-class classification: pre-invasive lesions (AAH-AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (ADC). Metrics include overall three-class accuracy and weighted F1 score.
At the time of availability of pathology results, up to 6 months after index chest CT
Matthews Correlation Coefficient (MCC) and Confusion Matrix for Invasion Classification
기간: At the time of availability of pathology results, up to 6 months after index chest CT
At the time of availability of pathology results, up to 6 months after index chest CT
Exploratory Performance of MVCSN Model for Malignancy Prediction
기간: At the time of availability of pathology results, up to 6 months after index chest CT
In a subset with complete clinical and imaging feature data, the MVCSN model (CT + clinical + imaging features) will be evaluated for malignancy prediction using similar metrics (AUC, sensitivity, specificity, PPV, NPV, accuracy, F1, calibration). Results will be used exploratorily and not for primary hypothesis testing.
At the time of availability of pathology results, up to 6 months after index chest CT

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

Guangdong Provincial People's Hospital

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (추정된)

2026년 7월 1일

기본 완료 (추정된)

2028년 3월 1일

연구 완료 (추정된)

2028년 12월 1일

연구 등록 날짜

최초 제출

2026년 7월 18일

QC 기준을 충족하는 최초 제출

2026년 7월 22일

처음 게시됨 (실제)

2026년 7월 27일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 7월 27일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 7월 22일

마지막으로 확인됨

2026년 7월 1일

추가 정보

이 연구와 관련된 용어

키워드

추가 관련 MeSH 약관

기타 연구 ID 번호

  • KY2026-585-02
  • 2024ZD0529400 (기타 보조금/기금 번호: National Science and Technology Major Project of China)

개별 참가자 데이터(IPD) 계획

개별 참가자 데이터(IPD)를 공유할 계획입니까?

미정

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .

구독하다