ArticleCancer imaging : the official publication of the International Cancer Imaging Society2024
Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Time-dependent diffusion MRI for differentiating cervical cancer subtypes: impact of ROI delineation strategies on diagnostic performance.Abdominal radiology (New York) · 2026Article
- MRI for Lung Cancer Management: Any Closer to Clinical Application?Journal of magnetic resonance imaging : JMRI · 2026Review
- The value of T1 mapping and intravoxel incoherent motion parameters in predicting PD-L1 expression and dynamically monitoring immunotherapy in advanced lung cancer.Frontiers in oncology · 2026Article
- The Impact of Artificial Intelligence on Lung Cancer Diagnosis and Personalized Treatment.International journal of molecular sciences · 2025Review
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5 authors.
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Abstract
backgroundThere is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC.
methodsSeventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model.
resultsADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P < 0.05). The mean value of each parameter was better than or equivalent to other histogram metrics, where the mean value of f obtained from whole tumor and single slice both had the highest AUC (AUC = 0.886 and 0.812, respectively) compared to other single parameters. The combined model improved the diagnostic efficiency with an AUC of 0.968 (whole tumor) and 0.893 (single slice), respectively.
conclusionsWhole tumor volume ROI demonstrated better diagnostic ability than single slice ROI analysis, which indicated whole tumor histogram analysis of IVIM and DKI hold greater potential than single slice ROI analysis to be a promising tool of predicting therapeutic response to chemoimmunotherapy in advanced NSCLC at initial state.
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