このページは自動翻訳されたものであり、翻訳の正確性は保証されていません。を参照してください。 英語版 ソーステキスト用。

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

2026年7月22日 更新者：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.

調査の概要

状態

まだ募集していません

条件

詳細な説明

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).

研究の種類

観察的

入学 (推定)

3000

連絡先と場所

このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。

研究連絡先

研究場所

  • 中国
    • Chongqing Municipality
      • Chongqing、Chongqing Municipality、中国、400037
        • Second Affiliated Hospital of Army Medical University (Xinqiao Hospital)
        • コンタクト：
    • Guangdong
      • Guangzhou、Guangdong、中国、510000
        • Guangdong Provincial People's Hospital
        • コンタクト：
        • 主任研究者：
          • Wenzhao Zhong, PhD
      • Guangzhou、Guangdong、中国、510000
        • Zhujiang Hospital, Southern Medical University
        • コンタクト：
    • Jiangsu
      • Xuzhou、Jiangsu、中国、221006
        • Affiliated Hospital of Xuzhou Medical University
        • コンタクト：
    • Zhejiang
      • Hangzhou、Zhejiang、中国、310003
        • Zhejiang University
        • コンタクト：

参加基準

研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。

適格基準

就学可能な年齢

  • 大人
  • 高齢者

健康ボランティアの受け入れ

いいえ

サンプリング方法

非確率サンプル

調査対象母集団

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

説明

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.

研究計画

このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。

研究はどのように設計されていますか？

デザインの詳細

コホートと介入

グループ/コホート
Single group
This study has only one group.

この研究は何を測定していますか？

主要な結果の測定

結果測定
メジャーの説明
時間枠
Area Under the ROC Curve (AUC) for Malignancy Prediction
時間枠：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

二次結果の測定

結果測定
メジャーの説明
時間枠
Sensitivity and Specificity for Malignancy Prediction
時間枠：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)
時間枠：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
時間枠：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
時間枠：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

その他の成果指標

結果測定
メジャーの説明
時間枠
Multi-Class Accuracy of MVCS for Pathological Invasion Degree
時間枠：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
時間枠：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
時間枠：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

協力者と研究者

ここでは、この調査に関係する人々や組織を見つけることができます。

スポンサー

Guangdong Provincial People's Hospital

研究記録日

これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。

主要日程の研究

研究開始 (推定)

2026年7月1日

一次修了 (推定)

2028年3月1日

研究の完了 (推定)

2028年12月1日

試験登録日

最初に提出

2026年7月18日

QC基準を満たした最初の提出物

2026年7月22日

最初の投稿 (実際)

2026年7月27日

学習記録の更新

投稿された最後の更新 (実際)

2026年7月27日

QC基準を満たした最後の更新が送信されました

2026年7月22日

最終確認日

2026年7月1日

詳しくは

本研究に関する用語

キーワード

追加の関連 MeSH 用語

その他の研究ID番号

  • KY2026-585-02
  • 2024ZD0529400 (その他の助成金/資金番号：National Science and Technology Major Project of China)

個々の参加者データ (IPD) の計画

個々の参加者データ (IPD) を共有する予定はありますか?

未定

医薬品およびデバイス情報、研究文書

米国FDA規制医薬品の研究

いいえ

米国FDA規制機器製品の研究

いいえ

この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。

購読する