Background Cancer patients have a high incidence of malnutrition, but traditional nutritional screening tools have low sensitivity and specificity, so they cannot properly stratify patient prognosis. Thus, we aimed to identify the nutritional indexes associated with patient prognosis, construct a prognostic model, and develop a nomogram for predicting individual survival probability. Methods Based on real-world data, patients admitted to the Department of Chemotherapy & Radiotherapy in Taizhou Cancer Hospital from January 1, 2017, to July 1, 2020, were included in the analysis. We collected nutritional indicators, clinicopathological characteristics, and previous major treatment details of the patients. The enrolled patients were randomly divided into training and validation cohorts in a 7:3 ratio. Lasso regression cross-validation was used in the training cohort to determine the variables to include in the Cox regression model. The training cohort was used to build the prediction model, and the validation cohort was used to further verify the discrimination, calibration and clinical effectiveness of the model. Results A total of 2,020 patients were included. The median follow-up time was 33.48 months (IQR, [15.79, 56.73] months), and the median OS was 56.50 months (95% CI, 50.36–62.65 months). In the training cohort of 1,425 patients, through Lasso regression cross-validation, thirteen characteristics were included in the model: sex, age, baseline weight, food intake reduction grade, emerging disease, ECOG performance status, hospitalization frequency, prealbumin, albumin, clinical stage, hemoglobin suppression grade, platelet suppression grade, and liver function classification. Based on these factors, a Cox proportional hazards model was developed and visualized as a nomogram. The C-indexes of the model for predicting 1-, 3-, 5- and 10-year OS were 0.848, 0.826, 0.814 and 0.799 in the training cohort and 0.851, 0.819, 0.814, and 0.801 in the validation cohort. The model showed great calibration in the two cohorts. Patients with a score of less than 274.29 had a better prognosis (training cohort: HR, 6.932; 95% CI, 5.723–8.397; log-rank P < 0.001; validation cohort: HR, 8.429; 95% CI, 6.180-11.497; log-rank P < 0.001). Conclusions The prognostic model based on the nutritional indexes of patients with pan-carcinomas can divide patients into different survival risk groups and performed well in internal validation.

Background We aimed to identify the nutritional indexes, construct a prognostic model, and develop a nomogram for predicting individual survival probability in pan-cancers. Methods We collected nutritional indicators, clinicopathological characteristics, and previous major treatment details of the patients. The enrolled patients were randomly divided into training and validation cohorts. Lasso regression cross-validation was used to determine the variables to include in the cox regression model. The training cohort was used to build the prediction model, and the validation cohort was used to further verify the discrimination, calibration and clinical effectiveness of the model. Results A total of 2,020 patients were included. The median OS was 56.50 months (95% CI, 50.36–62.65 months). In the training cohort of 1,425 patients, through Lasso regression cross-validation, thirteen characteristics were included in the model. Cox proportional hazards model was developed and visualized as a nomogram. The C-indexes of the model for predicting 1-, 3-, 5- and 10-year OS were 0.848, 0.826, 0.814 and 0.799 in the training cohort and 0.851, 0.819, 0.814, and 0.801 in the validation cohort. The model showed great calibration in the two cohorts. Patients with a score of less than 274.29 had a better prognosis (training cohort: HR, 6.932; 95% CI, 5.723–8.397; log-rank P < 0.001; validation cohort: HR, 8.429; 95% CI, 6.180-11.497; log-rank P < 0.001). Conclusions The prognostic model based on the nutritional indexes of pan-cancer can divide patients into different survival risk groups and performed well in validation cohort.

Objective To detect the appropriate fluoroscopic angle of intraoperative C-arm X-ray based on precise femoral neck anatomy. Methods The femoral neck was divided into the anterior, posterosuperior and posteroinferior surfaces. These surfaces and the coronal plane of the femur formed anterior surface coronal angle (ACA), posterosuperior surface coronal angle (PSCA) and posteroinferior surface coronal angle (PICA), respectively. The three angles of 32 dried femoral specimens were measured. The femoral neck wall attached with steel wire wind aluminum foil model and three Kirschner wires perforating femoral neck wall model were prepared. The C-arm was rotated every 5° to complete the 0° -180 ° fluoroscopy of each curved surface. 111 frames of images were obtained for each femoral specimen. The appropriate fluoroscopic angle of each surface was obtained, and the fluoroscopic images characteristics of Kirschner wire penetrating the femoral neck cortex on three appropriate fluoroscopic angles and the anteroposterior and lateral fluoroscopy were observed. Results The femoral neck is irregularly cylindrical with the anterior surface is longer than the posteroinferior surface, and the posterosuperior surface is the shortest. The measurement data of ACA, PSCA and PICA were (31±4.589)°, (67.813±5.052)° and (168.688±3.206)°, respectively. The appropriate fluoroscopic angle of the anterior, posterosuperior and posteroinferior surface of the steel wire aluminum foil model were (30.781±5.464)°, (67.969±3.721)°, (167.813±4.319)°, respectively. And there was no significant difference with the measurement data of the corresponding surface coronal angles, P > 0.05. The model of wire penetrating the femoral neck wall showed that the Kirschner wire penetrating the femoral neck could not be fully exposed in the traditional anteroposterior and lateral view films. Increasing the appropriate fluoroscopic angle of 30°, 70° and 170° could clearly find the Kirschner wire penetrating the cortex. Conclusion Traditional anteroposterior and lateral fluoroscopy cannot accurately show the true structure of femoral neck. Additional 30°, 70° and 170° fluoroscopy can accurately observe the fracture reduction quality of the anterior surface, posterosuperior surface and posteroinferior surface of the femoral neck and the damage to the corresponding cortical bone caused by internal fixation. Level of evidence: level II