P13.03.A Radiomics for the non-invasive assessment of the PDL-1 expression in patients with non-small cell lung cancer brain metastases

Meißner, Anna-Katharina; Gutsche, Robin; Galldiks, Norbert; Köcher, Martin; Jünger, Stephanie T.; Eich, Marie‐Lisa; Nogová, Lucia; Schmidt, Noemi; Ruge, M; Goldbrunner, Roland; Proescholdt, Martin; Grau, Stefan; Lohmann, Philipp

doi:10.1093/neuonc/noac174.283

Cited by 3 publications

(4 citation statements)

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“…The process of traditional radiomics includes image acquisition, reconstruction and preprocessing, region of interest delineation, manual feature extraction, feature selection, machine learning construction. Meißner developed a radiomics classifier to predict intracranial BRAF V600E mutation status in patients with melanoma brain metastases, and achieved an AUC value of 0.92 (38). Zhao implemented the World Health Organization (WHO) classification of meningiomas based on radiomics and clinical information, and the AUC value reached 0.860 (95% CI, 0.788-0.923) (39).…”

Section: Discussionmentioning

confidence: 99%

A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors

Wang,

He,

et al. 2024

Front. Oncol.

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ObjectivesThe diagnosis and treatment of brain tumors have greatly benefited from extensive research in traditional radiomics, leading to improved efficiency for clinicians. With the rapid development of cutting-edge technologies, especially deep learning, further improvements in accuracy and automation are expected. In this study, we explored a hybrid deep learning scheme that integrates several advanced techniques to achieve reliable diagnosis of primary brain tumors with enhanced classification performance and interpretability.MethodsThis study retrospectively included 230 patients with primary brain tumors, including 97 meningiomas, 66 gliomas and 67 pituitary tumors, from the First Affiliated Hospital of Yangtze University. The effectiveness of the proposed scheme was validated by the included data and a commonly used data. Based on super-resolution reconstruction and dynamic learning rate annealing strategies, we compared the classification results of several deep learning models. The multi-classification performance was further improved by combining feature transfer and machine learning. Classification performance metrics included accuracy (ACC), area under the curve (AUC), sensitivity (SEN), and specificity (SPE).ResultsIn the deep learning tests conducted on two datasets, the DenseNet121 model achieved the highest classification performance, with five-test accuracies of 0.989 ± 0.006 and 0.967 ± 0.013, and AUCs of 0.999 ± 0.001 and 0.994 ± 0.005, respectively. In the hybrid deep learning tests, LightGBM, a promising classifier, achieved accuracies of 0.989 and 0.984, which were improved from the original deep learning scheme of 0.987 and 0.965. Sensitivities for both datasets were 0.985, specificities were 0.988 and 0.984, respectively, and relatively desirable receiver operating characteristic (ROC) curves were obtained. In addition, model visualization studies further verified the reliability and interpretability of the results.ConclusionsThese results illustrated that deep learning models combining several advanced technologies can reliably improve the performance, automation, and interpretability of primary brain tumor diagnosis, which is crucial for further brain tumor diagnostic research and individualized treatment.

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Section: Discussionmentioning

confidence: 99%

A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors

Wang,

He,

et al. 2024

Front. Oncol.

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“…Some studies have reported that radiomics showed good performance in predicting the PD‐L1 expression status in several cancers. Meißner et al 32 reported that an MRI‐based radiomics model could predict intracranial PD‐L1 expression in patients with brain metastases secondary to non‐small cell lung cancer (NSCLC). A computed tomography (CT)‐based radiomics signature was reported to predict PD‐L1 expression in patients with advanced NSCLC 33 .…”

Section: Discussionmentioning

confidence: 99%

“…19,20 Some studies have reported that radiomics showed good performance in predicting the PD-L1 expression status in several cancers. Meißner et al 32 reported that an A profile plot was produced, and the 13, 15, and 18 best coefficients in the intratumoral, peritumoral, and combined radiomics signatures, respectively, were generated at the selected log (λ) by using a 10-fold cross-validation. MRI-based radiomics model could predict intracranial PD-L1 expression in patients with brain metastases secondary to nonsmall cell lung cancer (NSCLC).…”

Section: Discussionmentioning

confidence: 99%

Intratumoral and Peritumoral Radiomics Based on Preoperative MRI for Evaluation of Programmed Cell Death Ligand‐1 Expression in Breast Cancer

Wu,

Lin,

Wang

et al. 2023

Magnetic Resonance Imaging

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BackgroundProgrammed cell death ligand‐1 (PD‐L1) is a promising target for immune checkpoint blockade therapy in breast cancer. However, the preoperative evaluation of PD‐L1 expression in breast cancer is rarely explored.PurposeTo determine the ability of radiomics signatures based on preoperative dynamic contrast‐enhanced (DCE) MRI to evaluate PD‐L1 expression in breast cancer.Study TypeRetrospective.Population196 primary breast cancer patients with preoperative MRI and postoperative pathological evaluation of PD‐L1 expression, divided into training (n = 137, 28 PD‐L1‐positive) and test cohorts (n = 59, 12 PD‐L1‐positive).Field Strength/Sequence3.0T; volume imaging for breast assessment DCE sequence.AssessmentRadiomics features were extracted from the first phase of DCE‐MRI by using the minimum redundancy maximum relevance method and least absolute shrinkage and selection operator algorithm. Three radiomics signatures were constructed based on the intratumoral, peritumoral, and combined intra‐ and peritumoral regions. The performance of the signatures was assessed using area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and accuracy.Statistical TestsUnivariable and multivariable logistic regression analysis, t‐tests, chi‐square tests, Fisher exact test or Yates correction, ROC analysis, and one‐way analysis of variance. P < 0.05 was considered significant.ResultsIn the test cohort, the combined radiomics signature (AUC, 0.853) exhibited superior performance compared to the intratumoral (AUC, 0.816; P = 0.528) and peritumoral radiomics signatures (AUC, 0.846; P = 0.905) in PD‐L1 status evaluation, although the differences did not reach statistical significance.Data ConclusionIntratumoral and peritumoral radiomics signatures based on preoperative breast MRI showed some potential accuracy for the non‐invasive evaluation of PD‐L1 status in breast cancer.Level of Evidence4Technical EfficacyStage 2

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“…[17][18][19][20]. In recent years, there has been growing interest in using radiomics to predict the expression levels of relevant disease molecular biomarkers and disease prognosis, showing potential promising clinical applications [21][22][23][24].…”

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confidence: 99%

CT-based radiomics predicts CD38 expression: indirectly reflects clinical prognosis in epithelial ovarian cancer

Yao,

Zhang,

Liu

et al. 2023

Preprint

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Background Cluster of differentiation 38 (CD38) has been found to be highly expressed in various solid tumors, and its expression level may be associated with patient prognosis and survival. The study aimed to explore the prognostic value of CD38 expression for patients with epithelial ovarian cancer (EOC) and to construct two computed tomography (CT)-based radiomics models for CD38 expression prediction. Methods A total of 333 cases of EOC were enrolled from The Cancer Genome Atlas (TCGA) database for CD38 related bioinformatics and survival analysis. 56 intersection cases from TCGA and The Cancer Imaging Archive (TCIA) databases were selected for radiomics feature extraction and model construction. Logistic regression (LR) and support vector machine (SVM) models were constructed and internally validated using 5-fold cross-validation to assess the performance of the models for CD38 expression levels. Results High expression of CD38 was an independent protective factor (HR = 0.540) for overall survival (OS) in EOC patients. 5 radiomics features based on CT images were selected to build models for CD38 expression prediction. In the training set and internal validation set, for the receiver operating characteristic (ROC) Curve, the LR model reached area under the curve (AUC) of 0.739 and 0.732, while the SVM model achieved AUC values of 0.741 and 0.700, respectively. For the precision-recall (PR) Curve, the LR model and SVM model demonstrated AUC of 0.760 and 0.721. The calibration curves and decision curve analysis (DCA) provided evidence supporting the fitness and net benefit of the models. Conclusions High expression of CD38 can improve OS in EOC patients. CT-based radiomics models can be a new predictive tool for CD38 expression, offering possibilities for individualized survival assessment for patients with EOC.

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P13.03.A Radiomics for the non-invasive assessment of the PDL-1 expression in patients with non-small cell lung cancer brain metastases

Cited by 3 publications

References 0 publications

A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors

A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors

Intratumoral and Peritumoral Radiomics Based on Preoperative MRI for Evaluation of Programmed Cell Death Ligand‐1 Expression in Breast Cancer

CT-based radiomics predicts CD38 expression: indirectly reflects clinical prognosis in epithelial ovarian cancer

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