A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer.

OBJECTIVES: Synchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM. METHODS: A total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC). RESULTS: The AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874-0.978), 0.897 (95% CI: 0.801-0.994), 0.885 (95% CI: 0.795-0.975), and 0.889 (95% CI: 0.823-0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model. CONCLUSIONS: The radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans. CRITICAL RELEVANCE STATEMENT: The onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans. KEY POINTS: Prognosis for patients with CRPM is bleak, and early detection poses challenges. The synergy between radiomics and deep learning proves advantageous in evaluating CRPM. The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients.

Radiomics Based on Contrast-Enhanced CT for Recognizing c-Met-Positive Hepatocellular Carcinoma: a Noninvasive Approach to Predict the Outcome of Sorafenib Resistance.

OBJECTIVES: The purpose of our project was to investigate the effectiveness of radiomic features based on contrast-enhanced computed tomography (CT) that can detect the expression of c-Met in hepatocellular carcinoma (HCC) and to validate its efficacy in predicting the outcome of sorafenib resistance. MATERIALS AND METHODS: In total, 130 patients (median age, 60 years) with pathologically confirmed HCC who underwent contrast material-enhanced CT from October 2012 to July 2020 were randomly divided into a training set (n = 91) and a test set (n = 39). Radiomic features were extracted from arterial phase (AP), portal venous phase (VP) and delayed phase (DP) images of every participant's enhanced CT images. RESULTS: The entire group comprised 39 Met-positive and 91 Met-negative patients. The combined model, which included the clinical factors and the radiomic features, performed well in the training (area under the curve [AUC] = 0.878) and validation (AUC = 0.851) cohorts. The nomogram, which relied on the combined model, fits well in the calibration curves. Decision curve analysis (DCA) further confirmed that the clinical valuation of the nomogram achieved comparable accuracy in c-Met prediction. Among another 20 patients with HCC who had received sorafenib, the predicted high-risk group had shorter overall survival (OS) than the predicted low-risk group (p < 0.05). CONCLUSION: A multivariate model acquired from three phases (AP, VP and DP) of enhanced CT, HBV-DNA and γ glutamyl transpeptidase isoenzyme II (GGT-II) could be considered a satisfactory preoperative marker of the expression of c-Met in patients with HCC. This approach may help in overcoming sorafenib resistance in advanced HCC.

Application of Imaging Indicators Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Colorectal Peritoneal Carcinomatosis.

Objective: The imaging features of peritoneal carcinomatosis (PC) with different locations and pathological types of colorectal cancer (CRC) on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) were analyzed and discussed. Methods: The PET/CT data of 132 patients with colorectal peritoneal carcinomatosis (CRPC) who met the inclusion and exclusion criteria between May 30, 2016, and December 31, 2019, were collected and analyzed. Observations included the location and pathological type of CRC, the peritoneal cancer index (PCI), standardized uptake maximum value (SUVmax), and retention index (RI) of the CRPC. Statistical analysis was performed using SPSS 20.0 software, and P < 0.05 was considered statistically significant. Results: (1) The range of the PCI in the 132 patients studied was 2-30, with a mean value of 7.40 ± 8.14. The maximum long diameter of the CRPC lesions ranged from 0.6 to 12.1 cm, with an average of 3.23 ± 1.94 cm. The SUVmax ranged from 1.2 to 31.0, with a mean value of 9.65 ± 6.01. The SUVmax and size correlation coefficient for maximal CRPC lesions was r = 0.47 (P < 0.001). The RI range of the 72 patients who underwent time-lapse scanning was -10.0-112.2%, with RI quartiles of 13.5-48.9%; RI was ≥5% in 65 cases and <5% in seven cases. (2) The patients were grouped by the location of their CRC: the right-sided colon cancer (RCC, n = 37), left-sided colon cancer (LCC, n = 44), and rectal cancer groups (RC, n = 51). There were significant differences in the CRC pathological types (P = 0.009) and PCI scores (P = 0.02) between the RCC and RC groups and the RI between the RCC group and the other two groups (P < 0.001). (3) There were 88 patients organized into three groups by the pathology of their CRC: the moderately well-differentiated adenocarcinoma (group A, n = 57), poorly differentiated adenocarcinoma (group B, n = 16), and mucinous adenocarcinoma groups (group C, n = 15 cases, including one case of signet-ring cell carcinoma). There were significant differences in the CRC position (P = 0.003) and SUVmax (P = 0.03) between groups A and C. Conclusion: The PCI, SUVmax, and RI of peritoneal metastatic carcinoma caused by CRC in different locations and pathological types vary. Mucinous adenocarcinoma and poorly differentiated adenocarcinoma are relatively common in the right colon, and the PCI of peritoneal metastatic carcinoma is fairly high, but the SUVmax and RI are somewhat low.

A radiomics model based on DCE-MRI and DWI may improve the prediction of estimating IDH1 mutation and angiogenesis in gliomas.

PURPOSE: To investigate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) in estimating isocitrate dehydrogenase 1 (IDH1) mutation and angiogenesis in gliomas. METHOD: One hundred glioma patients with DCE-MRI and DWI were enrolled in this study (training and validation groups with a ratio of 7:3). The IDH1 genotypes and expression of vascular endothelial growth factor (VEGF) in gliomas were assessed by immunohistochemistry. Radiomics features were extracted by an open source software (3DSlicer) and reduced using Least absolute shrinkage and selection operator (Lasso). The support vector machine (SVM) model was developed based on the most useful predictive radiomics features. The conventional model was built by the selected clinical and morphological features. Finally, a combined model including radiomics signature, age and enhancement degree was established. Receiver operator characteristic (ROC) curve was implemented to assess the diagnostic performance of the three models. RESULTS: For IDH1 mutation, the combined model achieved the highest area under curve (AUC) in comparison with the SVM and conventional models (training group, AUC = 0.967, 0.939 and 0.906; validation group, AUC = 0.909, 0.880 and 0.842). Furthermore, the SVM model showed good diagnostic performance in estimating gliomas VEGF expression (validation group, AUC = 0.919). CONCLUSIONS: The radiomics model based on DCE-MRI and DWI can have a considerable effect on the evaluation of IDH1 mutation and angiogenesis in gliomas.