Preoperative Noninvasive Radiomics Approach Predicts Tumor Consistency in Patients With Acromegaly: Development and Multicenter Prospective Validation

Abstract

Background: Prediction of tumor consistency before surgery is of vital importance to determine individualized therapeutic schemes for patients with acromegaly. The present study was performed to noninvasively predict tumor consistency based on magnetic resonance imaging and radiomics analysis. Methods: In total, 158 patients with acromegaly were randomized into the primary cohort ( n = 100) and validation cohort ( n = 58). The consistency of the tumor was classified as soft or firm according to the neurosurgeon's evaluation. The critical radiomics features were determined using the elastic net feature selection algorithm, and the radiomics signature was constructed. The most valuable clinical characteristics were then selected based on the multivariable logistic regression analysis. Next, a radiomics model was developed using the radiomics signature and clinical characteristics, and 30 patients with acromegaly were recruited for multicenter validation of the radiomics model. The model's performance was evaluated based on the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, and other associated classification measures. Its calibration, discriminating capacity, and clinical usefulness were also evaluated. Results: The radiomics signature established according to four radiomics features screened in the primary cohort exhibited excellent discriminatory capacity in the validation cohort. The radiomics model, which incorporated both the radiomics signature and Knosp grade, displayed favorable discriminatory capacity and calibration, and the AUC was 0.83 (95% confidence interval, 0.81–0.85) and 0.81 (95% confidence interval, 0.78–0.83) in the primary and validation cohorts, respectively. Furthermore, compared with the clinical characteristics, the as-constructed radiomics model is more effective in prediction of the tumor consistency in patients with acromegaly. Moreover, the multicenter validation and decision curve analysis suggested that the radiomics model was clinically useful. Conclusions: This radiomics model can assist neurosurgeons in predicting tumor consistency in patients with acromegaly before surgery and facilitates the determination of individualized therapeutic schemes.

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

“…The tumor size and the Knosp grade were determined using preoperative pituitary contrast-enhanced MRI images ( 20 , 21 ). The cavernous sinus invasion ( 22 ) and the tumor texture ( 2 ) were determined by the surgeon during the operation. The cavernous sinus invasion of tumors was considered to be positive if the tumor extended the cavernous sinus and a cavernous sinus defect was observed ( 23 ).…”

“…The cavernous sinus invasion of tumors was considered to be positive if the tumor extended the cavernous sinus and a cavernous sinus defect was observed (23). Tumor that could be suctioned out using an aspirator was considered as soft, while a tumor that could not be suctioned out was considered as firm (2). The Ki-67 index was defined by an immunohistochemistry assay.…”

“…Acromegaly is a chronic endocrine disease that is mostly caused by growth hormone (GH)-secreting pituitary adenomas (PAs), resulting in excessive circulating levels of insulin-like growth factor 1 (IGF1) and in high morbidity and mortality (1,2). According to the current Endocrine Society Clinical Practice Guidelines on acromegaly, transsphenoidal surgery (TSS) is the first-line treatment, and its initial cure rate for macroadenomas is 40-50% (3).…”

Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients With Acromegaly

“…This nomogram is used based on the value of the patient's eight risk factors, including radiomics signature, random GH, IGF-1 standard deviation score (SDS), GH inhibition ratio (IR), tumor volume, Knosp grade, tumor consistency, and P53 value. As shown in our previous research (49), draw a vertical line from the corresponding axis of each factor until it reaches the first “Points” line. Next, summarize the points of all risk factors, then draw a vertical line that falls vertically from the “Total Points” axis until it reaches the last axis to determine the radiotherapeutic response.…”

Automatic detection of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms

“…Deep neural networks have been used to diagnose various diseases [9][10][11][12] . Specifically, most CNN model based research for pituitary diseases have been focused on pituitary macroadenoma [24][25][26] . Our work is the first study in applying CNN model for pituitary microadenomas diagnosis.…”