Development and validation of a 18F-FDG PET/CT radiomics nomogram for predicting progression free survival in locally advanced cervical cancer: a retrospective multicenter study.

BACKGROUND: The existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemotherapy (CCRT) can improve their treatment management. Thus, this present study aimed to develop and validate radiomics models based on pretreatment 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) images to accurately predict the prognosis in patients. METHODS: The data from 190 consecutive patients with LACC who underwent pretreatment 18F-FDG PET-CT and CCRT at two cancer hospitals were retrospectively analyzed; 176 patients from the same hospital were randomly divided into training (n = 117) and internal validation (n = 50) cohorts. Clinical features were selected from the training cohort using univariate and multivariate Cox proportional hazards models; radiomic features were extracted from PET and CT images and filtered using least absolute shrinkage and selection operator and Cox proportional hazard regression. Three prediction models and a nomogram were then constructed using the previously selected clinical, CT and PET radiomics features. The external validation cohort that was used to validate the models included 23 patients with LACC from another cancer hospital. The predictive performance of the constructed models was evaluated using receiver operator characteristic curves, Kaplan Meier curves, and a nomogram. RESULTS: In total, one clinical, one PET radiomics, and three CT radiomics features were significantly associated with progression-free survival in the training cohort. Across all three cohorts, the combined model displayed better efficacy and clinical utility than any of these parameters alone in predicting 3-year progression-free survival (area under curve: 0.661, 0.718, and 0.775; C-index: 0.698, 0.724, and 0.705, respectively) and 5-year progression-free survival (area under curve: 0.661, 0.711, and 0.767; C-index, 0.698, 0.722, and 0.676, respectively). On subsequent construction of a nomogram, the calibration curve demonstrated good agreement between actually observed and nomogram-predicted values. CONCLUSIONS: In this study, a clinico-radiomics prediction model was developed and successfully validated using an independent external validation cohort. The nomogram incorporating radiomics and clinical features could be a useful clinical tool for the early and accurate assessment of long-term prognosis in patients with LACC patients who undergo concurrent chemoradiotherapy.

Study on Heat Transfer Characteristics of Graphene Nanofluids in Mini-Channels of Thermal Integrated Building.

Two kinds of rectangular mini-channels of different sizes were designed and fabricated for testing the convective heat transfer characteristics of graphene nanofluids. The experimental results show that the average wall temperature decreases with the increases in graphene concentration and Re number at the same heating power. Within the experimental Re number range, the average wall temperature of 0.03% graphene nanofluids in the same rectangular channel decreases by 16% compared with that of water. At the same heating power, the convective heat transfer coefficient increases with the increase in the Re number. The average heat transfer coefficient of water can be increased by 46.7% when the mass concentration of graphene nanofluids is 0.03% and the rib-to-rib ratio is 1:2. In order to better predict the convection heat transfer characteristics of graphene nanofluids in small rectangular channels of different sizes, the convection heat transfer equations applicable to graphene nanofluids of different concentrations in small rectangular channels with different channel rib ratios were fitted, based on factors such as flow Re number, graphene concentration, channel rib ratio, Pr number, and Pe number; the average relative error (MRE) was 8.2%. The mean relative error (MRE) was 8.2%. The equations can thus describe the heat transfer characteristics of graphene nanofluids in rectangular channels with different groove-to-rib ratios.

Dosimetric analysis and biological evaluation between proton radiotherapy and photon radiotherapy for the long target of total esophageal squamous cell carcinoma.

Objective: The purpose of this study is to compare the dosimetric and biological evaluation differences between photon and proton radiation therapy. Methods: Thirty esophageal squamous cell carcinoma (ESCC) patients were generated for volumetric modulated arc therapy (VMAT) planning and intensity-modulated proton therapy (IMPT) planning to compare with intensity-modulated radiation therapy (IMRT) planning. According to dose-volume histogram (DVH), dose-volume parameters of the plan target volume (PTV) and homogeneity index (HI), conformity index (CI), and gradient index (GI) were used to analyze the differences between the various plans. For the organs at risk (OARS), dosimetric parameters were compared. Tumor control probability (TCP) and normal tissue complication probability (NTCP) was also used to evaluate the biological effectiveness of different plannings. Results: CI, HI, and GI of IMPT planning were significantly superior in the three types of planning (p < 0.001, p < 0.001, and p < 0.001, respectively). Compared to IMRT and VMAT planning, IMPT planning improved the TCP (p<0.001, p<0.001, respectively). As for OARs, IMPT reduced the bilateral lung and heart accepted irradiation dose and volume. The dosimetric parameters, such as mean lung dose (MLD), mean heart dose (MHD), V 5, V 10, and V 20, were significantly lower than IMRT or VMAT. IMPT afforded a lower maximum dose (D max) of the spinal cord than the other two-photon plans. What's more, the radiation pneumonia of the left lung, which was caused by IMPT, was lower than IMRT and VMAT. IMPT achieved the pericarditis probability of heart is only 1.73% ± 0.24%. For spinal cord myelitis necrosis, there was no significant difference between the three different technologies. Conclusion: Proton radiotherapy is an effective technology to relieve esophageal cancer, which could improve the TCP and spare the heart, lungs, and spinal cord. Our study provides a prediction of radiotherapy outcomes and further guides the individual treatment.

Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures.

PURPOSE: To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients. METHODS: 204 ESCC patients were randomly divided into training cohort (n = 143) and test cohort (n = 61) according to the ratio of 7:3. Two radiomics models were constructed by radiomics features, which were selected by LASSO Cox model to predict PFS and OS, respectively. Clinical features were selected by univariate and multivariate Cox proportional hazards model (p < 0.05). Combined radiomics and clinical model was developed by selected clinical and radiomics features. The receiver operating characteristic curve, Kaplan Meier curve and nomogram were used to display the capability of constructed models. RESULTS: There were 944 radiomics features extracted based on volume of interest in CT images. There were six radiomics features and seven clinical features for PFS prediction and three radiomics features and three clinical features for OS prediction; The radiomics models showed general performance in training cohort and test cohort for prediction for prediction PFS (AUC, 0.664, 0.676. C-index, 0.65, 0.64) and OS (AUC, 0.634, 0.646.C-index, 0.64, 0.65). The combined models displayed high performance in training cohort and test cohort for prediction PFS (AUC, 0.856, 0.833. C-index, 0.81, 0.79) and OS (AUC, 0.742, 0.768. C-index, 0.72, 0.71). CONCLUSION: We developed combined radiomics and clinical machine learning models with better performance than radiomics or clinical alone, which were used to accurate predict 3 years PFS and OS of non-surgical ESCC patients. The prediction results could provide a reference for clinical decision.