Dual-Energy Computed Tomography Parameters Combined With Inflammatory Indicators Predict Cervical Lymph Node Metastasis in Papillary Thyroid Cancer.

BACKGROUND AND OBJECTIVE: Cervical lymph node metastasis (CLNM) is considered a marker of papillar Fethicy thyroid cancer (PTC) progression and has a potential impact on the prognosis of PTC. The purpose of this study was to screen for predictors of CLNM in PTC and to construct a predictive model to guide the surgical approach in patients with PTC. METHODS: This is a retrospective study. Preoperative dual-energy computed tomography images of 114 patients with pathologically confirmed PTC between July 2019 and April 2023 were retrospectively analyzed. The dual-energy computed tomography parameters [iodine concentration (IC), normalized iodine concentration (NIC), the slope of energy spectrum curve (λHU)] of the venous stage cancer foci were measured and calculated. The independent influencing factors for predicting CLNM were determined by univariate and multivariate logistic regression analysis, and the prediction models were constructed. The clinical benefits of the model were evaluated using decision curves, calibration curves, and receiver operating characteristic curves. RESULTS: The statistical results show that NIC, derived neutrophil-to-lymphocyte ratio (dNLR), prognostic nutritional index (PNI), gender, and tumor diameter were independent predictors of CLNM in PTC. The AUC of the nomogram was .898 (95% CI: .829-.966), and the calibration curve and decision curve showed that the prediction model had good predictive effect and clinical benefit, respectively. CONCLUSION: The nomogram constructed based on dual-energy CT parameters and inflammatory prognostic indicators has high clinical value in predicting CLNM in PTC patients.

MRI combined with clinical features to differentiate ovarian thecoma-fibroma with cystic degeneration from ovary adenofibroma.

OBJECTIVE: To explore the value of magnetic resonance imaging (MRI) and clinical features in identifying ovarian thecoma-fibroma (OTF) with cystic degeneration and ovary adenofibroma (OAF). METHODS: A total of 40 patients with OTF (OTF group) and 28 patients with OAF (OAF group) were included in this retrospective study. Univariable and multivariable analyses were performed on clinical features and MRI between the two groups, and the receiver operating characteristic (ROC) curve was plotted to estimate the optimal threshold and predictive performance. RESULTS: The OTF group had smaller cyst degeneration degree (P < .001), fewer black sponge sign (20% vs. 53.6%, P = .004), lower minimum apparent diffusion coefficient value (ADCmin) (0.986 (0.152) vs. 1.255 (0.370), P < .001), higher age (57.4 ± 14.2 vs. 44.1 ± 15.9, P = .001) and more postmenopausal women (72.5% vs. 28.6%, P < .001) than OAF. The area under the curve of MRI, clinical features and MRI combined with clinical features was 0.870, 0.841, and 0.954, respectively, and MRI combined with clinical features was significantly higher than the other two (P < .05). CONCLUSION: The cyst degeneration degree, black sponge sign, ADCmin, age and menopause were independent factors in identifying OTF with cystic degeneration and OAF. The combination of MRI and clinical features has a good effect on the identification of the two. ADVANCES IN KNOWLEDGE: This is the first time to distinguish OTF with cystic degeneration from OAF by combining MRI and clinical features. It shows the diagnostic performance of MRI, clinical features, and combination of the two. This will facilitate the discriminability and awareness of these two diseases among radiologists and gynaecologists.

Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients.

OBJECTIVE: To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS). METHODS: Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated. RESULTS: Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms' predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms. CONCLUSION: CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.

Development of a nomogram based on body composition analysis of quantitative computed tomography combined with clinical prognostic factors to predict disease-free survival after surgery and adjuvant chemotherapy in patients with gastric cancer.

Background: Patients with gastric cancer (GC) have a high recurrence rate after surgery. To predict disease-free survival (DFS), we investigated the value of body composition changes (BCCs) measured by quantitative computed tomography (QCT) in assessing the prognosis of patients with GC undergoing resection combined with adjuvant chemotherapy and to construct a nomogram model in combination with clinical prognostic factors (CPFs). Methods: A retrospective study of 60 patients with GC between February 2015 and June 2019 was conducted. Pre- and posttreatment CT images of patients was used to measure bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA), and the rate of BCC was calculated. CPFs such as maximum tumor diameter (MTD), human epidermal growth factor receptor-2 (HER2), and Ki-67 were derived from postoperative pathological findings. Independent prognostic factors affecting DFS in GC were screened via univariate and multivariate Cox regression analysis. The Kaplan-Meier method and log-rank test were used to plot survival curves and compare the curves between groups, respectively. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves to evaluate the efficacy of the nomogram. Results: The results of multivariate Cox regression analysis showed that ΔBMD [hazard ratio (HR): 4.577; 95% confidence interval (CI): 1.483-14.132; P=0.008], ΔPMA (HR: 5.784; 95% CI: 1.251-26.740; P=0.025), HER2 (HR: 4.819; 95% CI: 2.201-10.549; P<0.001), and maximal tumor diameter (HR: 3.973; 95% CI: 1.893-8.337; P<0.001) were independent factors influencing DFS. ΔBMD, ΔSFA, ΔVFA, ΔTFA, and ΔPMA were -3.86%, -23.44%, -19.57%, -22.45%, and -5.94%, respectively. The prognostic model of BCCs combined with CPFs had the highest predictive performance. Decision curve analysis (DCA) indicated good clinical benefit for the prognostic nomogram. The concordance index of the prognostic nomogram was 0.814, and the area under the curve (AUC) of predicting 2- and 3-year DFS were 0.879 and 0.928, respectively. The calibration curve showed that the nomogram-predicted DFS aligned well with the actual DFS. Conclusions: The prognostic nomogram combining BCCs and CPFs was able to reliably predict the DFS of patients with GC.

Body composition predicts prognosis of hepatocellular carcinoma patients undergoing immune checkpoint inhibitors.

PURPOSE: Immune checkpoint inhibitors (ICIs) with anti-PD-1/PD-L1 antibody are promising treatments for hepatocellular carcinoma (HCC), but lack reliable biomarkers of response. In the present study, we aimed to investigate the correlation between pre-treatment body composition measures (muscle, adipose, etc.) and the prognosis of patients with HCC treated with ICIs. METHODS: We measured the total area of all skeletal muscles, total adipose tissue area, subcutaneous adipose tissue area, and visceral adipose tissue area at the level of the third lumbar vertebra using quantitative CT. Then, we calculated the skeletal muscle index, visceral adipose tissue index, subcutaneous adipose tissue index (SATI), and total adipose tissue index. The Cox regression model was used to determine the independent factors of the patient prognosis and construct a nomogram to predict survival. The consistency index (C-index) and calibration curve were used to determine the predictive accuracy and discrimination ability of the nomogram. RESULTS: Multivariate analysis revealed that the SATI (high- vs. low SATI; HR 0.251; 95% CI 0.109-0.577; P = 0.001), sarcopenia (sarcopenia vs. no sarcopenia; HR 2.171; 95% CI 1.100-4.284; P = 0.026), and portal vein tumor thrombus (PVTT) (PVTT vs. No PVTT; HR 2.429; 95% CI 1. 197-4. 929; P = 0.014) were indicated as independent prognostic factors for OS in multivariate analysis. Multivariate analysis indicated that Child-Pugh class (HR 0.477, 95% CI 0.257-0.885, P = 0.019) and sarcopenia (HR 2.376, 95% CI 1.335-4.230, P = 0.003) were independent prognostic factors of PFS. We established a nomogram using SATI, SA, and PVTT to predict the 12-month and 18-month survival probability of HCC patients treated with ICIs. The C-index of the nomogram was 0.754 (95% CI 0.686-0.823), and the calibration curve confirmed that the predicted results were in good agreement with the actual observations. CONCLUSION: Subcutaneous adipose and sarcopenia are significant prognostic factors of patients with HCC receiving ICIs. A nomogram based on body composition parameters and clinical factors could well predict survival in HCC patients treated with ICIs.

Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters.

Objective: To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM). Methods: A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T2WI, DWI, and contrast-enhanced T1WI (CE-T1WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model. Results: The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs. Conclusion: A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.