Preliminary exploration of the correlation between spectral computed tomography quantitative parameters and spread through air spaces in lung adenocarcinoma.

Background: The invasive pattern called spread through air spaces (STAS) is linked to an unfavorable prognosis in patients with lung adenocarcinoma (LUAD). Using computed tomography (CT) signs alone to assess STAS is subjective and lacks quantitative evaluation, whereas spectral CT can provide quantitative analysis of tumors. The aim of this study was to investigate the association between spectral CT quantitative parameters and STAS in LUAD. Methods: We retrospectively collected consecutive patients with LUAD who underwent surgical resection and preoperative spectral CT scan at our institution. The quantitative parameters included CT values at 40, 70, and 100 keV [CT40keVa/v, CT70keVa/v, and CT100keVa/v (a: arterial; v: venous)]; iodine concentration (ICa/ICv); normalized iodine concentration (NICa/NICv); and slope λHU of the spectral curve (λHUa/λHUv). Clinical and CT features of the patients were also collected. Statistical analysis was performed to identify the quantitative parameters, clinical and CT features that were significantly correlated with STAS status. We evaluated the diagnostic performance of significant factors or models which combined quantitative parameters and CT features, using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results: We enrolled a total of 47 patients, with 32 positive and 15 negative for STAS. The results revealed that CT100keVa (P=0.002), CT100keVv (P=0.007), pathologic stage (P=0.040), tumor density (P<0.001), spiculation (P=0.003), maximum solid component diameter (P=0.008), and the consolidation/tumor ratio (CTR) (P=0.001) were significantly correlated with STAS status. The tumor density demonstrated a superior diagnostic capability [AUC =0.824, 95% confidence interval (CI): 0.709-0.939, sensitivity =59.4%, specificity =100.0%] compared to other variables. CT100keVa exhibited the best diagnostic performance (AUC =0.779, 95% CI: 0.633-0.925, sensitivity =78.1%, specificity =80.0%) among the quantitative parameters. Combination models were then constructed by combining the quantitative parameters with CT features. The total combined model showed the highest diagnostic efficiency (AUC =0.952, 95% CI: 0.894-1.000, sensitivity =90.6%, specificity =86.7%). Conclusions: Spectral CT quantitative parameters CT100keVa and CT100keVv may be potentially useful parameters in distinguishing the STAS status in LUAD.

Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study.

OBJECTIVE: To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients. METHODS: We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model's performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan-Meier survival curve to analyze the prognosis of BCa patients. RESULTS: The combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659-1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival. CONCLUSION: The proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively. CRITICAL RELEVANCE STATEMENT: Bladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients. KEY POINTS: • Conventional imaging is not sufficiently accurate to determine lymph node status. • Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis. • The proposed method showed satisfactory patient risk stratification for progression-free survival.

CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.

BACKGROUND: To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively. METHODS: We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: The support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature. CONCLUSION: The proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.