Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging parameters and serum tumor markers in rectal carcinoma prognosis.

BACKGROUND: Rectal carcinoma (RC), one of the most common malignancies globally, presents an increasing incidence and mortality year by year, especially among young people, which seriously affects the prognosis and quality of life of patients. At present, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and serum carbohydrate antigen 19-9 (CA19-9) and CA125 Levels have been used in clinical practice to evaluate the T stage and differentiation of RC. However, the accuracy of these evaluation modalities still needs further research. This study explores the application and value of these methods in evaluating the T stage and differentiation degree of RC. AIM: To analyze the diagnostic performance of DCE-MRI parameters combined with serum tumor markers (TMs) in assessing pathological processes and prognosis of RC patients. METHODS: A retrospective analysis was performed on 104 RC patients treated at Yantai Yuhuangding Hospital from May 2018 to January 2022. Patients were categorized into stages T1, T2, T3, and T4, depending on their T stage and differentiation degree. In addition, they were assigned to low (L group) and moderate-high differentiation (M + H group) groups based on their differentiation degree. The levels of DCE-MRI parameters and serum CA19-9 and CA125 in different groups of patients were compared. In addition, the value of DCE-MRI parameters [volume transfer constant (Ktrans), rate constant (Kep), and extravascular extracellular volume fraction (Ve) in assessing the differentiation and T staging of RC patients was discussed. Furthermore, the usefulness of DCE-MRI parameters combined with serum CA19-9 and CA125 Levels in the evaluation of RC differentiation and T staging was analyzed. RESULTS: Ktrans, Ve, CA19-9 and CA125 were higher in the high-stage group and L group than in the low-stage group and M + H Group, respectively (P < 0.05). The areas under the curve (AUCs) of the Ktran and Ve parameters were 0.638 and 0.694 in the diagnosis of high and low stages, respectively, and 0.672 and 0.725 in diagnosing moderate-high and low differentiation, respectively. The AUC of DCE-MRI parameters (Ktrans + Ve) in the diagnosis of high and low stages was 0.742, and the AUC in diagnosing moderate-high and low differentiation was 0.769. The AUCs of CA19-9 and CA-125 were 0.773 and 0.802 in the diagnosis of high and low stages, respectively, and 0.834 and 0.796 in diagnosing moderate-high and low differentiation, respectively. Then, we combined DCE-MRI (Ktrans + Ve) parameters with CA19-9 and CA-125 and found that the AUC of DCE-MRI parameters plus serum TMs was 0.836 in the diagnosis of high and low stages and 0.946 in the diagnosis of moderate-high and low differentiation. According to the Delong test, the AUC of DCE-MRI parameters plus serum TMs increased significantly compared with serum TMs alone in the diagnosis of T stage and differentiation degree (P < 0.001). CONCLUSION: The levels of the DCE-MRI parameters Ktrans and Ve and the serum TMs CA19-9 and CA125 all increase with increasing T stage and decreasing differentiation degree of RC and can be used as indices to evaluate the differentiation degree of RC in clinical practice. Moreover, the combined evaluation of the above indices has a better effect and more obvious clinical value, providing important guiding importance for clinical condition judgment and treatment selection.

Combined model of radiomics, clinical, and imaging features for differentiating focal pneumonia-like lung cancer from pulmonary inflammatory lesions: an exploratory study.

BACKGROUND: Only few studies have focused on differentiating focal pneumonia-like lung cancer (F-PLC) from focal pulmonary inflammatory lesion (F-PIL). This exploratory study aimed to evaluate the clinical value of a combined model incorporating computed tomography (CT)-based radiomics signatures, clinical factors, and CT morphological features for distinguishing F-PLC and F-PIL. METHODS: In total, 396 patients pathologically diagnosed with F-PLC and F-PIL from two medical institutions between January 2015 and May 2021 were retrospectively analyzed. Patients from center 1 were included in the training (n = 242) and internal validation (n = 104) cohorts. Moreover, patients from center 2 were classified under the external validation cohort (n = 50). The clinical and CT morphological characteristics of both groups were compared first. And then, a clinical model incorporating clinical and CT morphological features, a radiomics model reflecting the radiomics signature of lung lesions, and a combined model were developed and validated, respectively. RESULTS: Age, gender, smoking history, respiratory symptoms, air bronchogram, necrosis, and pleural attachment differed significantly between the F-PLC and F-PIL groups (all P < 0.05). For the clinical model, age, necrosis, and pleural attachment were the most effective factors to differentiate F-PIL from F-PLC, with the area under the curves (AUCs) of 0.838, 0.819, and 0.717 in the training and internal and external validation cohorts, respectively. For the radiomics model, five radiomics features were found to be significantly related to the identification of F-PLC and F-PIL (all P < 0.001), with the AUCs of 0.804, 0.877, and 0.734 in the training and internal and external validation cohorts, respectively. For the combined model, five radiomics features, age, necrosis, and pleural attachment were independent predictors for distinguishing between F-PLC and F-PIL, with the AUCs of 0.915, 0.899, and 0.805 in the training and internal and external validation cohorts, respectively. The combined model exhibited a better performance than had the clinical and radiomics models. CONCLUSIONS: The combined model, which incorporates CT-based radiomics signatures, clinical factors, and CT morphological characteristics, is effective in differentiating F-PLC from F-PIL.

Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma.

BACKGROUND: Based on the "seed and soil" theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC). METHODS: Radiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (-) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model's performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed. RESULTS: Thirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691-0.927 in the training cohort and 0.700-0.951 in the validation cohort, respectively, thereby indicating good performance. CONCLUSION: CT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.