Aims/Background In the treatment of patients with cervical cancer, lymph node metastasis (LNM) is an important indicator for stratified treatment and prognosis of cervical cancer. This study aimed to develop and validate a multimodal model based on contrast-enhanced multiphase computed tomography (CT) images and clinical variables to accurately predict LNM in patients with cervical cancer. Methods This study included 233 multiphase contrast-enhanced CT images of patients with pathologically confirmed cervical malignancies treated at the Affiliated Dongyang Hospital of Wenzhou Medical University. A three-dimensional MedicalNet pre-trained model was used to extract features. Minimum redundancy-maximum correlation, and least absolute shrinkage and selection operator regression were used to screen the features that were ultimately combined with clinical candidate predictors to build the prediction model. The area under the curve (AUC) was used to assess the predictive efficacy of the model. Results The results indicate that the deep transfer learning model exhibited high diagnostic performance within the internal validation set, with an AUC of 0.82, accuracy of 0.88, sensitivity of 0.83, and specificity of 0.89. Conclusion We constructed a comprehensive, multiparameter model based on the concept of deep transfer learning, by pre-training the model with contrast-enhanced multiphase CT images and an array of clinical variables, for predicting LNM in patients with cervical cancer, which could aid the clinical stratification of these patients via a noninvasive manner.
Objectives: To compare the perioperative, functional, and oncologic outcomes of robot-assisted partial nephrectomy (RAPN) and laparoscopic partial nephrectomy (LPN) for completely endophytic renal tumors (three points for the "E" element of the R.E.N.A.L. scoring system). Materials and Methods: We retrospectively reviewed patients who underwent either RAPN or LPN between 2013 and 2016. Baseline characteristics, perioperative, functional, and oncologic outcomes were compared. Univariable and multivariable logistic analyses were performed to determine factors associated with pentafecta achievement (ischemia time ≤25 minutes, negative margin, no perioperative complication, return of estimated glomerular filtration rate [eGFR] to >90% from baseline, and no chronic kidney disease upstaging). Results: No significant differences between RAPN vs LPN were noted for operating time (105 minutes vs 108 minutes, p = 0.916), estimated blood loss (50 mL vs 50 mL, p = 0.130), renal artery clamping time (20 minutes vs 20 minutes, p = 0.695), rate of positive margins (3.3% vs 2.0%, p = 1.000), and postoperative complication rates (18.0% vs 21.6%, p = 0.639). RAPN was associated with a higher direct cost ($11240 vs $5053, p < 0.001). There were no significant differences in pathology variables, rate of eGFR decline for postoperative 12-month (9.8% vs 10.6%, p = 0.901) functional follow-up. Multivariate analysis identified that only RENAL score was independently associated with the pentafecta achievement. Conclusions: For completely endophytic renal tumors, both RAPN and LPN have excellent and similar results. Both operation techniques remain viable options in the management of these cases.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)