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  11. Predicting Durable Clinical Benefits Of Postoperative Adjuvant Chemotherapy In Non-small Cell Lung Cancer: A Nomogram Based On Ct Imaging And Immune Type.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Predicting Durable Clinical Benefits Of Postoperative Adjuvant Chemotherapy In Non-small Cell Lung Cancer: A Nomogram Based On Ct Imaging And Immune Type.

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Predicting Durable Clinical Benefits of Postoperative Adjuvant Chemotherapy in Non-small Cell Lung Cancer: A Nomogram Based on CT Imaging and Immune Type.

Liangna Deng1, Mingtao Zhang2, Kaibo Zhu1

  • 1Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China.

Academic Radiology
|August 17, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
Durable clinical benefitsNon-small cell lung cancerTumor microenvironment immune typesX-ray computed tomography

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This study developed a model using CT scans and tumor microenvironment immune types (TIMT) to predict durable clinical benefits from chemotherapy in non-small cell lung cancer (NSCLC) patients, showing promising results for outcome prediction.

Area of Science:

  • Oncology
  • Radiology
  • Immunology

Background:

  • Non-small cell lung cancer (NSCLC) treatment often involves adjuvant chemotherapy.
  • Predicting durable clinical benefits (DCB) from chemotherapy is crucial for optimizing patient outcomes.
  • Conventional CT signs and tumor microenvironment immune types (TIMT) are potential biomarkers.

Purpose of the Study:

  • To develop and validate a predictive model for DCB in NSCLC patients undergoing adjuvant chemotherapy.
  • The model integrates conventional CT findings with TIMT.
  • To assess the model's performance using ROC curves, calibration curves, and DCA.

Main Methods:

  • 205 NSCLC patients were analyzed, categorized into DCB (≥18 months PFS) and non-DCB (<18 months PFS) groups.
  • TIMT was estimated by quantifying PD-L1 and CD8+ TIL density.
  • Multivariate logistic regression identified key predictors (TIMT, cM stage, LYMPH, pleural effusion) for nomogram construction.

    • Main Results:

    • TIMT, clinical characteristics (cM stage), and CT signs (pleural effusion) differed significantly between DCB and non-DCB groups.
    • A nomogram incorporating TIMT, cM stage, LYMPH, and pleural effusion was developed.
    • The combined model achieved an AUC of 0.70, with 73% sensitivity and 60% specificity for predicting DCB.

    Conclusions:

    • Conventional CT signs combined with TIMT provide a promising method for predicting clinical outcomes in NSCLC patients receiving adjuvant chemotherapy.
    • The developed nomogram can aid in stratifying patients likely to benefit from treatment.
    • Further validation is warranted to refine clinical utility.