Multimodal Model for Non-Invasive Detection of DRD2, SSTR2 and ESR1 Receptor Profiling in Pituitary Neuroendocrine Tumors: A Retrospective Study.

Introduction: The dopamine receptor D2 (DRD2), somatostatin receptor 2 (SSTR2), and oestrogen receptor 1 (ESR1) have been demonstrated to play a critical role in determining treatment response in pituitary neuroendocrine tumors (PitNETs). However, the identification of these receptors preoperative presented a significant challenge. The objective of this study was to develop a predictive model that employs both radiomics and deep learning features in conjunction with conventional magnetic resonance imaging (MRI) to predict the expression of these three receptors in PitNETs in a retrospective study. Materials and Methods: A total of 186 patients with complete imaging data (coronal T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI) were included for model construction (training set, n = 148; validation set, n = 38). Semiquantitative analysis of re-verse transcription polymerase chain reaction and immunohistochemistry of the samples was performed to complete the classification of high or low expressions of these three drug targets in patients. A multimodal model was validated using a receiver operating characteristic analysis on an independent validation set. Results: The dynamic multi-layer perceptron (MLP) classifier showed an area under the curve (AUC) of 0.9571 (DRD2), 0.9191 (SSTR2), and 0.9485 (ESR1) in the training set and an AUC of 0.9260 (DRD2), 0.9084 (SSTR2), and 0.9409 (ESR1) in the validation set, which fitted well with the training set. The dynamic MLP classifier achieved the highest performance among all the individual models in the validation set. Conclusions: The dynamic MLP classifier can noninvasively predict the expression of key targets of PitNETs, which will help guide clinical drug treatment decisions.