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Hongjiang Pu,1,* Peiyi Xie,2,* Yaxue Chen,3,* Yanrong Zhao,4 Xi Ye,5 Guiyu Lu,4 Dafu Zhang,6 Zhenhui Li6 1Department of Oncology, Dazhou Central Hospital, Dazhou, Sichuan, 635000, People’s Republic of China; 2Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, People’s Republic of China; 3Department of Nursing, Dazhou Vocational and Technical College, Dazhou, Sichuan, 635000, People’s Republic of China; 4Department of Colorectal Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Centre, Kunming, 650118, People’s Republic of China; 5Department of Oncology, The First People’s Hospital of Honghe State, Mengzi, 661100, People’s Republic of China; 6Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Centre, Kunming, 650118, People’s Republic of China*These authors contributed equally to this workCorrespondence: Dafu Zhang; Zhenhui LiDepartment of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Centre, Kunming, Yunnan, 650118, People’s Republic of ChinaTel/ Fax +86 87168179594; +86 87168179549Email tosney@126.com; lizhenhui621@qq.comPurpose: Based on a multi-centered and a large sample size, this study aims to analyze the relationship between preoperative and postoperative serum CEA and recurrence of rectal cancer without preoperative therapy.Methods: This retrospective cohort study enrolled stage I to III rectal cancer patients without preoperative therapy (N = 1,022) who received radical resection of rectal cancer from 2 hospitals in China. Based on the preoperative and postoperative serum carcinoembryonic antigen, the patients were subdivided into 3 groups ie, normal preoperative CEA (≤ 5.0 ng/mL, N = 627), elevated preoperative (> 5.0 ng/mL) but normalized postoperative CEA (normalized postoperative CEA, N = 255), as well as elevated preoperative and postoperative CEA (elevated postoperative CEA, N = 67). The generalized additive model was used to assess the relationship between carcinoembryonic antigen and the risk of recurrence. Further, the Cox regression model was used to evaluate the relationship between carcinoembryonic antigen and 3-year recurrence-free survival (RFS) after adjusting for potential confounders.Results: The 3-year RFS of patients with elevated postoperative CEA was 45.8% (95% CI, 35.2% − 59.5%), which was significantly lower compared to the other two groups of patients (normalized postoperative CEA: 75.9%, 95% CI, 70.8%-81.4%; and normal preoperative CEA: 84.9%, 95% CI, 82.2%-87.8%) (P < 0.001). Based on multivariable Cox model analysis, the elevated postoperative CEA was a prognostic factor for 3 years RFS (hazard ratio [HR], 3.08; 95% CI, 2.05– 4.66; P< 0.001). At the same time, normalized postoperative CEA was insignificantly correlated with 3-year RFS (HR, 1.38; 95% CI, 1.00– 1.92; P = 0.05) and was not an independent risk factor.Conclusion: We found that preoperative and postoperative serum CEA of rectal cancer patients were related to the 3-year recurrence-free survival rate. Moreover, the risk of recurrence in the normalized postoperative CEA group of patients was insignificantly different from that of the normalized preoperative CEA patients. Therefore, it is necessary to combine preoperative and postoperative CEA to predict the prognosis of patients with rectal cancer, rather than using it alone.Keywords: preoperative and postoperative serum CEA, rectal cancer, RFS

Purpose This study aimed to develop and validate a nomogram for predicting the malignancy of small (8-20 mm) solid indeterminate solitary pulmonary nodules (SPNs) in a Chinese population by using routine clinical and computed tomography data. Methods The prediction model was developed using a retrospective cohort that comprised 493 consecutive patients with small indeterminate SPNs who were treated between December 2012 and December 2016. The model was independently validated using a second retrospective cohort comprising 216 consecutive patients treated between January 2017 and May 2018. The investigated variables included patient characteristics (e.g., age and smoking history), nodule parameters (e.g., marginal spiculation and significant enhancement), and tumor biomarker levels (e.g., carcinoembryonic antigen). A prediction model was developed by using multivariable logistic regression analysis, and the model's performance was presented as a nomogram. The model was evaluated based on its discriminative ability, calibration, and clinical usefulness. Results The developed nomogram was ultimately based on age, marginal spiculation, significant enhancement, and pleural indentation. The Harrell concordance index values were 0.869 in the training cohort (95% confidence interval: 0.837-0.901) and 0.847 in the validation cohort (95% confidence interval: 0.792-0.902). The Hosmer-Lemeshow test revealed good calibration in each of the training and validation cohorts. Decision curve analysis confirmed that the nomogram was clinically useful (risk threshold from 0.10 to 0.85). Conclusion Patient age, marginal spiculation, significant enhancement, and pleural indentation are independent predictors of malignancy in small indeterminate solid SPNs. The developed nomogram is easy-to-use and may allow the accurate prediction of malignancy in small indeterminate solid SPNs among Chinese patients.

Background Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics. Methods We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months’ postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram. Findings DLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM (P Interpretation MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT. Funding The funding bodies that contributed to this study are listed in the Acknowledgements section.

Purpose: This study aimed to develop and validate a nomogram for predicting 3-year disease-free survival (DFS) among a multicenter cohort of Chinese patients with locally advanced rectal cancer (LARC) who underwent preoperative therapy followed by surgery. This nomogram might help identify patients who would benefit from postoperative adjuvant chemotherapy and close follow-up. Materials and methods: All data from 228 patients in two independent Chinese cohorts (118 patients and 110 patients) were pooled and subjected to survival analysis. One cohort’s data were used to develop multivariate nomograms based on Cox regression, and the second cohort was used for external validation. The variables were sex, age, clinical tumor stage, tumor location, preoperative therapy protocol, adjuvant chemotherapy, surgical procedure, surgical approach, pTNM stage, tumor deposit, tumor regression grade, lymphovascular invasion, perineural invasion, pretreatment serum carcinoembryonic antigen (CEA) level, preoperative CEA level, and postoperative CEA level. The model’s performance was evaluated based on its discrimination, calibration, and clinical usefulness. Results: The nomogram was based on ypT stage and ypN stage, and the C-index values for 3-year DFS were 0.70 in the training cohort (95% confidence interval: 0.62–0.78) and 0.78 in the validation cohort (95% confidence interval: 0.68–0.89). The Hosmer-Lemeshow calibration test revealed good calibration for predicting 3-year DFS in the training and validation cohorts, and decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion: This nomogram including the ypT stage and ypN stage could predict DFS at 3 years after surgery, which may help better identify Chinese patients who would benefit from additional postoperative adjuvant systemic treatment.

Supplemental digital content is available in the text., Objective The aim of the study was to assess the value of cinematic rendering (CR) from volumetric computed tomography data in evaluating the relationship between deep soft tissue sarcomas (STSs) of the extremities and the adjacent major vessels. Methods Preoperative contrast-enhanced axial imaging (CEAI) in the arterial phase with three-dimensional volume rendering (VR) and CR of contrast-enhanced computed tomography were used to assess adjacent vascular invasion in 43 cases of deep STSs of the extremities. The imaging assessments were compared with surgical findings and interpreted as negative (no vascular invasion) or positive (vascular invasion was present). Intrareader and interreader agreement were assessed using Cohen κ statistics. The diagnostic performance of CEAI, VR, and CR was evaluated by receiver operating curve analysis and compared using the DeLong test. Results Thirty-four and nine cases were classified as negative and positive, respectively, in surgery. Intrareader agreement values for the CEAI, VR, and CR assessments were all excellent (0.984, 0.934, and 0.914, respectively), whereas the interreader agreement for CEAI assessments was greater than that for VR and CR (0.969 vs 0.804 and 0.761). Cinematic rendering showed lower accuracy (0.698), sensitivity (0.778), specificity (0.676), positive predictive values (0.389), and negative predictive values (0.920) for vascular invasion diagnosis than CEAI or VR; the accuracy, sensitivity, specificity, positive predictive values, and negative predictive values increased to 0.767, 0.889, 0.735, 0.471, and 0.962 for both CEAI and VR. The results were not statistically significant (all P > 0.05). Conclusions Cinematic rendering has the potential to be used to evaluate vascular invasion in cases of deep STSs of the extremities, but it should be used alongside the traditional methods such as CEAI.

Objective The standard treatment for patients with locally advanced gastric cancer has relied on perioperative radio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of radiomics for pre-treatment computed tomography (CT) in the prediction of the pathological response of locally advanced gastric cancer with preoperative chemotherapy. Methods Thirty consecutive patients with CT-staged II/III gastric cancer receiving neoadjuvant chemotherapy were enrolled in this study between December 2014 and March 2017. All patients underwent upper abdominal CT during the unenhanced, late arterial phase (AP) and portal venous phase (PP) before the administration of neoadjuvant chemotherapy. In total, 19,985 radiomics features were extracted in the AP and PP for each patient. Four methods were adopted during feature selection and eight methods were used in the process of building the classifier model. Thirty-two combinations of feature selection and classification methods were examined. Receiver operating characteristic (ROC) curves were used to evaluate the capability of each combination of feature selection and classification method to predict a non-good response (non-GR) based on tumor regression grade (TRG). Results The mean area under the curve (AUC) ranged from 0.194 to 0.621 in the AP, and from 0.455 to 0.722 in the PP, according to different combinations of feature selection and the classification methods. There was only one cross-combination machine-learning method indicating a relatively higher AUC (>0.600) in the AP, while 12 cross-combination machine-learning methods presented relatively higher AUCs (all >0.600) in the PP. The feature selection method adopted by a filter based on linear discriminant analysis + classifier of random forest achieved a significantly prognostic performance in the PP (AUC, 0.722±0.108; accuracy, 0.793; sensitivity, 0.636; specificity, 0.889; Z=2.039; P=0.041). Conclusions It is possible to predict non-GR after neoadjuvant chemotherapy in locally advanced gastric cancers based on the radiomics of CT.

Zhen-Hui Li,1,* Ding-Yun You,2,* De-Pei Gao,1 Guang-Jun Yang,1 Xing-Xiang Dong,1 Da-Fu Zhang,1 Ying-Ying Ding1 1Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Tumor Hospital of Yunnan Province, Kunming, 2Department of Science and Technology, Kunming Medical University, Kunming, Yunnan, People’s Republic of China *These authors contributed equally to this work Objectives: Most colorectal cancers are classical adenocarcinomas (AC), and less frequent subtypes include mucinous adenocarcinomas (MAC) and signet-ring cell carcinomas (SC). The purpose of this study was to evaluate the computed tomography (CT) findings that can help to differentiate MAC and SC from AC.Methods: CT scans of 168 patients with pathologically proven MAC and 67 patients with pathologically proven SC were analyzed, and 220 patients with classical AC were also included as a control group. CT findings of the three groups were compared and contrasted in terms of the bowel involvement patterns, contrast enhancement patterns, and presence or absence of bowel obstruction, intratumoral calcification, pericolic fat infiltration, and local tumor extension to adjacent organs. Statistical analyses were made by using the one-way analysis of variance, least significant difference test, and Pearson’s chi-square test.Results: Compared with classical AC, the MAC showed more severe (6.29±2.69 cm vs 4.57±1.74 cm, P

Da-Fu Zhang,* Zhen-Hui Li,* De-Pei Gao, Guang-Jun Yang, Ying-Ying Ding, Guan-Shun Wang, Xing-Xiang Dong Department of Radiology, The Third Hospital of Kunming Medical University, Tumor Hospital of Yunnan Province, Kunming, People’s Republic of China *These authors contributed equally tothis work Abstract: Ewing’s sarcoma/primary neuroectodermal tumor (EWS/PNET) is an extraordinarily rare primary tumor of the kidney with characteristic histology. To date, the imaging features of EWS/PNET have not been clearly described. Here, we report two cases of EWS/PNET confirmed by fine-needle aspiration biopsy and analyze the findings of computed tomography and ultrasound. The radiological features of EWS/PNET are presented along with a brief review of the pertinent literature to have a further understanding of EWS/PNET’s imaging features. Keywords: Ewing’s sarcoma, neuroectodermal tumor, X-ray computed tomography, histology, ultrasound