Predicting surgical outcome in posterior retroperitoneoscopic adrenalectomy with the aid of a preoperative nomogram

Background Posterior retroperitoneoscopic adrenalectomy (PRA) has several advantages over transperitoneal laparoscopic adrenalectomy (TLA) regarding operative time, blood loss, postoperative pain, and recovery. However, it can be a technically challenging procedure. To improve patient selection for PRA, we developed a preoperative nomogram to predict operative time. Methods All consecutive patients with tumors of ≤ 7 cm and a body mass index (BMI) of < 35 kg/m2 undergoing unilateral PRA between February 2011 and March 2020 were included in the study. The primary outcome was operative time as surrogate endpoint for surgical complexity. Using ten patient variables, an optimal prediction model was created, with a best subsets regression analysis to find the best one-variable up to the best seven-variable model. Results In total 215 patients were included, with a mean age of 52 years and mean tumor size of 2.4 cm. After best subsets regression analysis, a four-variable nomogram was selected and calibrated. This model included sex, pheochromocytoma, BMI, and perinephric fat, which were all individually significant predictors. This model showed an ideal balance between predictive power and applicability, with an R2 of 38.6. Conclusions A four-variable nomogram was developed to predict operative time in PRA, which can aid the surgeon to preoperatively identify suitable patients for PRA. If the nomogram predicts longer operative time and therefore a more complex operation, TLA should be considered as an alternative approach since it provides a larger working space. Also, the nomogram can be used for training purposes to select patients with favorable characteristics when learning this surgical approach.

The Uncertain Benefit of Adjuvant Chemotherapy in Advanced Low-Grade Serous Ovarian Cancer and the Pivotal Role of Surgical Cytoreduction

In our center, adjuvant chemotherapy is routinely offered in high-grade serous ovarian cancer (HGSOC) patients but less commonly as a standard treatment in low-grade serous ovarian cancer (LGSOC) patients. This study evaluates the efficacy of this paradigm by analysing survival outcomes and by comparing the influence of different clinical and surgical characteristics between women with advanced LGSOC (n = 37) and advanced HGSOC (n = 300). Multivariate analysis was used to identify independent prognostic features for survival in LGSOC and HGSOC. Adjuvant chemotherapy was given in 99.7% of HGSOC patients versus in 27% of LGSOC (p < 0.0001). The LGSOC patients had greater surgical complexity scores (p < 0.0001), more frequent postoperative ICU/HDU admissions (p = 0.0002), and higher peri-/post-operative morbidity (p < 0.0001) compared to the HGSOC patients. The 5-year OS and progression-free survival (PFS) was 30% and 13% for HGSOC versus 57% and 21.6% for LGSOC, p = 0.016 and p = 0.044, respectively. Surgical complexity (HR 5.3, 95%CI 1.2–22.8, p = 0.024) and complete cytoreduction (HR 62.4, 95% CI 6.8–567.9, p < 0.001) were independent prognostic features for OS in LGSOC. This study demonstrates no clear significant survival advantage of chemotherapy in LGSOC. It highlights the substantial survival benefit of dynamic multi-visceral surgery to achieve complete cytoreduction as the primary treatment for LGSOC patients.

What to choose and why to use – a critical review on the clinical relevance of rASRM, EFI and Enzian classifications of endometriosis

Background: Endometriosis is a common benign gynaecological disease that affects pelvic structures and causes adhesions. Endometriosis outside the pelvis exists but is rarer. Deep endometriosis may affect organs such as the urinary bladder, ureters, bowel and sacral roots. Adenomyosis (growth of endometrium in the myometrium, sometimes explained by disruption of the uterine junctional zone) frequently co-exists with deep endometriosis. Over the past decades, multiple attempts have been made to describe the anatomical extent of endometriosis. Out of approximately 20 classification systems suggested and published so far, three have gained widespread acceptance. These are the rASRM (American Society of Reproductive Medicine) classification, the Endometriosis Fertility Index (EFI) and the Enzian classification. Ideally, a classification system should be useful both for describing disease extent based on surgical findings and results of imaging methods (ultrasound, magnetic resonance imaging). Objectives: To highlight the advantages and disadvantages of the three classification systems. Methods: This is a narrative review based on selected publications and experience of the authors. We discuss the current literature on the use of the rASRM, EFI and Enzian classification systems for describing disease extent with imaging methods and for prediction of fertility, surgical complexity, and risk of surgical complications. We underline the need for one universally acceptable terminology to describe the extent of endometriosis. Conclusions: A useful classification system for endometriosis should describe the sites and extent of the disease, be related to surgical complexity and to disease-associated symptoms, including subfertility and should satisfy needs of both, imaging specialists for pre-operative classification and surgeons. The need for such a system is obvious and is provided by the #Enzian classification. Future research is necessary to test its validity.

Renal cell carcinoma with tumor thrombus growing against the direction of venous return: an indicator of complicated surgery and poor prognosis

Purpose To explore the effect of tumor thrombus growing against the direction of venous return (GADVR) tumor thrombus on the choice of surgical approach, the impact on the complexity of the surgery and the prognosis. Methods We retrospectively analyzed the clinicopathological data of 213 patients, who underwent surgery in a single center of Peking University Third Hospital between January 2016 and June 2020. For right renal cell carcinoma (RCC) and venous tumor thrombus (VTT), imaging revealed a filling defect in the left renal vein, which was significantly enhanced in enhanced imaging, suggesting that the tumor thrombus grew against the direction of venous return into the left renal vein. For left RCC and VTT, at least one of the left renal vein branches has tumor thrombus. The branches include the left adrenal vein, the left gonadal vein (testicular vein or ovarian vein), and the left ascending lumbar vein. The patients were divided into two groups according to whether they were GADVR tumor thrombus, and we compare the clinicopathological characteristics of GADVR tumor thrombus and non-GADVR tumor thrombus. Univariate and multivariate Cox regression analyses were performed to explore the risk factors that affect the prognosis of patients with RCC and VTT. Kaplan–Meier plots were conducted to evaluate the effect of GADVR on progression-free survival (PFS). Results Compared with non-GADVR tumor thrombus, patients with GADVR tumor thrombus had a higher proportion of open surgery (76.2% vs. 52.1%, P = 0.035), a higher proportion of tumor thrombus adhering to the inferior vena cava (IVC) vessel wall (81% vs. 45.8%, P = 0.002), a higher proportion of segmental resection of the IVC vessel wall (61.9% vs. 15.6%, P < 0.001); higher preoperative serum creatinine value (110.0 μmol/L vs. 91.0 μmol/L, P = 0.015), a higher proportion of tumor thrombus combined with bland thrombus (non-tumor thrombus) (57.1% vs. 19.8%, P < 0.001). In terms of surgical complexity, patients with GADVR tumor thrombus had a longer median operation time (379 min vs. 308 min, P = 0.038), more median surgical blood loss (1400 mL vs. 600 mL, P = 0.018), and more postoperative complications (52.4% vs. 30.7%, P = 0.045). Multivariate Cox regression analysis showed that GADVR tumor thrombus, symptoms, postoperative serum creatinine, distant metastasis, sarcomatoid feature, pathological type, lymph node dissection were independent risk factors for PFS. Patients with GADVR tumor thrombus’s median survival time was 14.0 months, while patients with non-GADVR tumor thrombus were 32.0 months (P = 0.016). GADVR tumor thrombus is an independent risk factor for PFS in patients with RCC and VTT. Conclusion GADVR tumor thrombus is a characteristic feature of tumor thrombus, with an incidence of 9.9%. It has a higher proportion of open surgery and higher surgical complexity, which is an independent risk factor for PFS.

O02 THE ROLE OF DEEP LEARNING IN PREDICTING COMPLEXITY AND COMPLICATIONS IN ABDOMINAL WALL RECONSTRUCTION

Aim The aim of our study was to evaluate the utility of image-based deep learning models (DLMs) to predict surgical complexity and postoperative outcomes in patients undergoing AWR. Material and Methods A prospective, tertiary center, hernia database was queried for open AWR patients with adequate pre-operative CT-scans. An 8-layer convolutional neural network (CNN) analyzed image characteristics in Python utilizing the open source Tensorflow© and OpenCV frameworks. Images were analyzed and batched into a training set (80%) and validation set (20%) used to analyze the model output, which was blinded to the CNN until testing. DLMs were run to assess surgical complexity based on need for component separation, surgical site infection (SSI), and pulmonary failure. The surgical complexity DLM was validated by comparison to 6 expert AWR surgeons. Results In total, 369 patient CT scans were utilized. The surgical complexity DLM performed well (ROC=0.744;p&lt;0.0001), and when compared to surgeon prediction on the validation set, performed better with an accuracy of 81.3% compared to 65.0% (p &lt; 0.0001). The SSI DLM was successful with an ROC of 0.898 (p &lt; 0.0001). The DLM for predicting pulmonary failure was less effective with an ROC of 0.545 (p = 0.03). Conclusions DLMs were able to successfully predict surgical complexity and were more accurate than expert surgeons using objective, pre-operative imaging. DLMs were also successful in predicting SSI. This breakthrough may allow for enhanced pre-operative planning, including resource utilization and possible need for tertiary center referral. AI appears to be an exciting new management tool in complex AWR.