New predictive factors for prolonged operation time of laparoscopic posterior retroperitoneal adrenalectomy; retrospective cohort study

Adrenalectomy for primary aldosteronism and its related surgical characteristics

Primary aldosteronism (PA) is a common cause of secondary hypertension. Adrenalectomy is an effective treatment for unilateral PA, particularly aldosterone-producing adenoma (APA), resulting in improvements in biochemical parameters and blood pressure in the vast majority of patients. The article provides a comprehensive overview of PA, focusing on the outcomes of adrenalectomy for PA and the factors that may suggest prognostic implications. Analysis of the outcome of different PA patients undergoing adrenalectomy in terms of preoperative factors, vascular and adipose conditions, type of pathology, and somatic variants. In addition, it is recommended to use the histopathology of primary aldosteronism (HISTALDO) consensus to classify the patient's pathological type, with classical and nonclassical pathological types showing a different prognosis and possibly being associated with an unresected contralateral adrenal gland. The primary aldosteronism surgical outcome (PASO) consensus sets uniform standards for postoperative outcomes in unilateral PA, but its setting of thresholds remains controversial. Partial adrenalectomy shows similar surgical results and fewer postoperative complications than total adrenalectomy, but there is a risk of missing the true source of abnormal aldosterone secretion. Steroid profiling and functional imaging techniques offer alternative options to adrenal vein sampling (AVS) for unilateral and bilateral judgments in patients with PA. A combination of factors is needed to predict the prognosis of PA patients undergoing adrenalectomy in order to manage patient expectations of the outcome of the procedure and to closely monitor blood pressure and biochemical parameters in patients who suggest a poorer prognosis.

Exploring the Benefits of a Reduced-Port Approach in Robotic Posterior Retroperitoneoscopic Adrenalectomy: A Comparative Study of the Two-Port and Three-Port Techniques

Background: Robotic adrenalectomy has become a surgical treatment option for benign and selected malignant adrenal diseases. We aimed to evaluate the eligibility of two-port robotic posterior retroperitoneoscopic adrenalectomy (PRA) as an alternative to the conventional three-port technique by comparing their surgical outcomes. Materials and Methods: This retrospective cohort study compared the clinicopathological factors and surgical outcomes among 197 patients who underwent two-port or three-port robotic adrenalectomy between 2016 and 2020 in a single tertiary center. For further evaluation, propensity score matching was performed to reduce the selection bias in population characteristics. Results: Patients were categorized by the number of ports (two-port group, 87; and three-port group, 110). The two-port group compared with the three-port group was significantly older (P = .006) and had a smaller mean tumor size (P = .003) and shorter mean operation time (P = .001). Upon comparing clinicopathologic characteristics according to adrenal disorders, for pheochromocytoma, the three-port group had a larger tumor size and a longer operation time. For Cushing's syndrome, the operation time was short and numeric rating scale pain score was significantly low in the two-port group. After propensity score matching, the two-port group had a short operation time and a significantly low postoperative pain score (P < .05). Predictive factors associated with prolonged operation time included male gender, an increased number of ports, and large tumor size. Conclusions: The two-port technique resulted in a shorter operation time and lower pain score compared with the three-port technique. The two-port technique may be a safe alternative to the conventional three-port technique for robotic PRA.

Laparoscopic transperitoneal adrenalectomy for adrenal tumours of 6 cm or greater: A single-centre experience

BACKGROUND

The present study aimed to evaluate the safety and efficacy of transperitoneal laparoscopic adrenalectomy (LA) for large adrenal tumours by comparing the outcomes of tumours larger than 6 cm with those smaller than 6 cm and also to identify the risk factors associated with prolonged operative time in transperitoneal LA.

PATIENTS AND METHODS

One hundred and sixty-three patients underwent LA at our clinic from January 2014 to December 2020. Bilateral LA was performed in 20 of these 163 patients. A total of 143 patients were included in this study. Data were analysed retrospectively from the patients' medical records collected.

RESULTS

Large tumour (LT) group consists of 33 patients and the small tumour (ST) group consists of 110 patients. There was no statistically significant difference between the groups regarding conversion to open surgery and complications. A multiple regression analysis was conducted to identify the independent predictors of prolonged operation time. The tumour size ≥8 cm (odds ratio [OR], 19.132; 95% confidence interval [CI], 3.881-94.303; P < 0.001) and diagnosis of pheochromocytoma (OR, 2.762; 95% CI, (1.123-6.789, P = 0.026) were the significant predictors of prolonged operation time.

CONCLUSION

Our study shows that LA can be considered the treatment of choice for small and large adrenal tumours. The tumour size ≥8 cm and diagnosis of pheochromocytoma are the independent risk factors for the prolonged operative time in transperitoneal LA.

Development and validation of machine-learning models for the difficulty of retroperitoneal laparoscopic adrenalectomy based on radiomics

Objective

The aim is to construct machine learning (ML) prediction models for the difficulty of retroperitoneal laparoscopic adrenalectomy (RPLA) based on clinical and radiomic characteristics and to validate the models.

Methods

Patients who had undergone RPLA at Shanxi Bethune Hospital between August 2014 and December 2020 were retrospectively gathered. They were then randomly split into a training set and a validation set, maintaining a ratio of 7:3. The model was constructed using the training set and validated using the validation set. Furthermore, a total of 117 patients were gathered between January and December 2021 to form a prospective set for validation. Radiomic features were extracted by drawing the region of interest using the 3D slicer image computing platform and Python. Key features were selected through LASSO, and the radiomics score (Rad-score) was calculated. Various ML models were constructed by combining Rad-score with clinical characteristics. The optimal models were selected based on precision, recall, the area under the curve, F1 score, calibration curve, receiver operating characteristic curve, and decision curve analysis in the training, validation, and prospective sets. Shapley Additive exPlanations (SHAP) was used to demonstrate the impact of each variable in the respective models.

Results

After comparing the performance of 7 ML models in the training, validation, and prospective sets, it was found that the RF model had a more stable predictive performance, while xGBoost can significantly benefit patients. According to SHAP, the variable importance of the two models is similar, and both can reflect that the Rad-score has the most significant impact. At the same time, clinical characteristics such as hemoglobin, age, body mass index, gender, and diabetes mellitus also influenced the difficulty.

Conclusion

This study constructed ML models for predicting the difficulty of RPLA by combining clinical and radiomic characteristics. The models can help surgeons evaluate surgical difficulty, reduce risks, and improve patient benefits.

A nomogram for evaluation and analysis of difficulty in retroperitoneal laparoscopic adrenalectomy: A single-center study with prospective validation using LASSO-logistic regression

BACKGROUND

While it is known that inaccurate evaluation for retroperitoneal laparoscopic adrenalectomy (RPLA) can affect the surgical results of patients, no stable and effective prediction model for the procedure exists. In this study, we aimed to develop a computed tomography (CT) -based radiological-clinical prediction model for evaluating the surgical difficulty of RPLA.

METHOD

Data from 398 patients with adrenal tumors treated by RPLA in a single center from August 2014 to December 2020 were retrospectively analyzed and divided into sets. The influencing factors were selected by least absolute shrinkage and selection operator regression model (LASSO). Additionally, the nomogram was constructed. A receiver operating characteristic curve was used to analyze the prediction efficiency of the nomogram. The C-index and bootstrap self-sampling methods were used to verify the discrimination and consistency of the nomogram.

RESULT

The following 11 independent influencing factors were selected by LASSO: body mass index, diabetes mellitus, scoliosis, hyperlipidemia, history of operation, tumor diameter, distance from adrenal tumor to upper pole of kidney, retro renal fat area, hyperaldosteronism, pheochromocytoma and paraganglioma, and myelolipoma. The area under the curve (AUC) of the training set was 0.787, and 0.844 in the internal validation set. Decision curve analyses indicated the model to be useful. An additional 117 patients were recruited for prospective validation, and AUC was 0.848.

CONCLUSION

This study developed a radiological-clinical prediction model proposed for predicting the difficulty of RPLA procedures. This model was suitable, accessible, and helpful for individualized surgical preparation and reduced operational risk. Thus, this model could contribute to more patients' benefit in circumventing surgical difficulties because of accurate predictive abilities.