Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions.

BACKGROUND: Classifying and characterizing pulmonary lesions are critical for clinical decision-making process to identify optimal therapeutic strategies. The purpose of this study was to develop and validate a radiomics nomogram for distinguishing between benign and malignant pulmonary lesions based on robust features derived from diffusion images. MATERIAL AND METHODS: The study was conducted in two phases. In the first phase, we prospectively collected 30 patients with pulmonary nodule/mass who underwent twice EPI-DWI scans. The robustness of features between the two scans was evaluated using the concordance correlation coefficient (CCC) and dynamic range (DR). In the second phase, 139 patients who underwent pulmonary DWI were randomly divided into training and test sets in a 7:3 ratio. Maximum relevance minimum redundancy, least absolute shrinkage and selection operator, and logistic regression were used for feature selection and construction of radiomics signatures. Nomograms were established incorporating clinical features, radiomics signatures, and ADC(0, 800). The diagnostic efficiency of different models was evaluated using the area under the curve (AUC) and decision curve analysis. RESULTS: Among the features extracted from DWI and ADC images, 42.7% and 37.4% were stable (both CCC and DR ≥ 0.85). The AUCs for distinguishing pulmonary lesions in the test set for clinical model, ADC, ADC radiomics signatures, and DWI radiomics signatures were 0.694, 0.802, 0.885, and 0.767, respectively. The nomogram exhibited the best differentiation performance (AUC = 0.923). The decision curve showed that the nomogram consistently outperformed ADC value and clinical model in lesion differentiation. CONCLUSION: Our study demonstrates the robustness of radiomics features derived from lung DWI. The ADC radiomics nomogram shows superior clinical net benefits compared to conventional clinical models or ADC values alone in distinguishing solitary pulmonary lesions, offering a promising tool for noninvasive, precision diagnosis in lung cancer.

Application of CD34 expression combined with three-phase dynamic contrast-enhanced computed tomography scanning in preoperative staging of gastric cancer.

BACKGROUND: Accurate preoperative staging of gastric cancer (GC), a common malignant tumor worldwide, is critical for appropriate treatment plans and prognosis. Dynamic three-phase enhanced computed tomography (CT) scanning for preoperative staging of GC has limitations in evaluating tumor angiogenesis. CD34, a marker on vascular endothelial cell surfaces, is promising in evaluating tumor angiogenesis. We explored the value of their combination for preoperative staging of GC to improve the efficacy and prognosis of patients with GC. AIM: To explore the evaluation value of CD34 expression + dynamic three-phase enhanced CT scanning in preoperative staging of GC. METHODS: Medical records of 106 patients with GC treated at the First People's Hospital of Lianyungang between February 2021 and January 2023 were retrospectively studied. All patients underwent three-phase dynamic contrast-enhanced CT scanning before surgery, and CD34 was detected in gastroscopic biopsy specimens. Using surgical and pathological results as the gold standard, the diagnostic results of three-phase dynamic contrast-enhanced CT scanning at different T and N stages were analyzed, and the expression of CD34-marked microvessel density (MVD) at different T and N stages was determined. The specificity and sensitivity of three-phase dynamic contrast-enhanced CT and CD34 in T and N staging were calculated; those of the combined diagnosis of the two were evaluated in parallel. Independent factors affecting lymph node metastasis were analyzed using multiple logistic regression. RESULTS: The accuracy of three-phase dynamic contrast-enhanced CT scanning in diagnosing stages T1, T2, T3 and T4 were 68.00%, 75.00%, 79.41%, and 73.68%, respectively, and for diagnosing stages N0, N1, N2, and N3 were 75.68%, 74.07%, 85.00%, and 77.27%, respectively. CD34-marked MVD expression increased with increasing T and N stages. Specificity and sensitivity of three-phase dynamic contrast-enhanced CT in T staging were 86.79% and 88.68%; for N staging, 89.06% and 92.86%; for CD34 in T staging, 64.15% and 88.68%; and for CD34 in N staging, 84.38% and 78.57%, respectively. Specificity and sensitivity of joint diagnosis in T staging were 55.68% and 98.72%, and N staging were 75.15% and 98.47%, respectively, with the area under the curve for diagnosis improving accordingly. According to multivariate analysis, a longer tumor diameter, higher pathological T stage, lower differentiation degree, and higher expression of CD34-marked MVD were independent risk factors for lymph node metastasis in patients with GC. CONCLUSION: With high accuracy in preoperatively determining the invasion depth and lymph node metastasis of GC, CD34 expression and three-phase dynamic contrast-enhanced CT can provide a reliable basis for surgical resection.