Machine Learning for Adrenal Gland Segmentation and Classification of                     Normal and Adrenal Masses at CT

Robinson-Weiss, Cory; Patel, Jay; Bizzo, Bernardo C.; Glazer, Daniel I.; Bridge, Christopher P.; Andriole, Katherine P.; Dabiri, Borna E.; Chin, John K.; Dreyer, Keith J.; Kalpathy–Cramer, Jayashree; Mayo-Smith, William W.

doi:10.1148/radiol.220101

Cited by 12 publications

(4 citation statements)

References 23 publications

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Section: Discussionmentioning

confidence: 99%

“…AI-related research in clinical health care has gained an incredible amount of traction over the past decade due to the increased availability and utilization of big data, the advancements in computing power and the advent of deep learning algorithms. 21 In the CV subdomain, deep learning models have been used to analyze images generated in radiology, 22 pathology, 23 and endoscopy studies. 24 In this study, we demonstrated the feasibility of using deep learning algorithms to recognize the LAV during laparoscopic transabdominal left adrenalectomy with a high level of performance.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence in Minimally Invasive Adrenalectomy: Using Deep Learning to Identify the Left Adrenal Vein

Sengun

İşcan

Özbulak

et al. 2023

Surgical Laparoscopy, Endoscopy &Amp; Percutaneous Techniques

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Background: Minimally invasive adrenalectomy is the main surgical treatment option for the resection of adrenal masses. Recognition and ligation of adrenal veins are critical parts of adrenal surgery. The utilization of artificial intelligence and deep learning algorithms to identify anatomic structures during laparoscopic and robot-assisted surgery can be used to provide real-time guidance. Methods: In this experimental feasibility study, intraoperative videos of patients who underwent minimally invasive transabdominal left adrenalectomy procedures between 2011 and 2022 in a tertiary endocrine referral center were retrospectively analyzed and used to develop an artificial intelligence model. Semantic segmentation of the left adrenal vein with deep learning was performed. To train a model, 50 random images per patient were captured during the identification and dissection of the left adrenal vein. A randomly selected 70% of data was used to train models while 15% for testing and 15% for validation with 3 efficient stage-wise feature pyramid networks (ESFPNet). Dice similarity coefficient (DSC) and intersection over union scores were used to evaluate segmentation accuracy. Results: A total of 40 videos were analyzed. Annotation of the left adrenal vein was performed in 2000 images. The segmentation network training on 1400 images was used to identify the left adrenal vein in 300 test images. The mean DSC and sensitivity for the highest scoring efficient stage-wise feature pyramid network B-2 network were 0.77 (±0.16 SD) and 0.82 (±0.15 SD), respectively, while the maximum DSC was 0.93, suggesting a successful prediction of anatomy. Conclusions: Deep learning algorithms can predict the left adrenal vein anatomy with high performance and can potentially be utilized to identify critical anatomy during adrenal surgery and provide real-time guidance in the near future.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Minimally Invasive Adrenalectomy: Using Deep Learning to Identify the Left Adrenal Vein

Sengun

İşcan

Özbulak

et al. 2023

Surgical Laparoscopy, Endoscopy &Amp; Percutaneous Techniques

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“…To date, the morphometrics of normal adrenal glands has not been studied in a relatively large sample. Recent deep learning-based studies mainly focused on the comparison between normal and abnormal adrenal glands [ 29 , 30 ]. The present study established a 3D U-Net [ 31 ]-based model for the automatic segmentation of adrenal glands in abdominal CT images and tried to discover the changes in morphometrics of normal adrenal glands in different age groups.…”

Section: Introductionmentioning

confidence: 99%

Age-related morphometrics of normal adrenal glands based on deep learning-aided segmentation

Chen

Yang

Zhang³

et al. 2023

Heliyon

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“…It can also be used to predict the health status of patients, assisting doctors to develop better treatment plans. In addition, ML can be used for drug development and clinical trials to speed up the development and launch of new drugs ( 11 ). For example, a study has used ML to differentiate between adrenal pheochromocytoma and adrenocortical adenoma ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

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

Sun,

Yao,

Wang

et al. 2023

Front. Endocrinol.

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ObjectiveThe 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.MethodsPatients 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.ResultsAfter 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.ConclusionThis 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.

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Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT

Cited by 12 publications

References 23 publications

Artificial Intelligence in Minimally Invasive Adrenalectomy: Using Deep Learning to Identify the Left Adrenal Vein

Artificial Intelligence in Minimally Invasive Adrenalectomy: Using Deep Learning to Identify the Left Adrenal Vein

Age-related morphometrics of normal adrenal glands based on deep learning-aided segmentation

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

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