Development of machine learning models integrating PET/CT radiomic and immunohistochemical pathomic features for treatment strategy choice of cervical cancer with negative pelvic lymph node by mediating COX-2 expression - Zhang, Zhe; Li, Xiaoran; Sun, Hongzan (Frontiers in Physiology, 2022)
Objectives: We aimed to establish machine learning models based on texture analysis predicting pelvic lymph node metastasis (PLNM) and expression of cyclooxygenase-2 (COX-2) in cervical cancer with PET/CT negative pelvic lymph node (PLN). Methods: Eight hundred and thirty-seven texture features were extracted from PET/CT images of 148 early-stage cervical cancer patients with negative PLN. The machine learning models were established by logistic regression from selected features and evaluated by the area under the curve (AUC). The correlation of selected PET/CT texture features predicting PLNM or COX-2 expression and the corresponding immunohistochemical (IHC) texture features was analyzed by the Spearman test. Results: Fourteen texture features were reserved to calculate the Rad-score for PLNM and COX-2. The PLNM model predicting PLNM showed good prediction accuracy in the training and testing dataset (AUC=0.817, p<0.001; AUC= 0.786, p<0.001, respectively). The COX-2 model also behaved well for predicting COX-2 expression levels in the training dataset and testing dataset (AUC=0.814, p<0.001; AUC=0.748, p=0.001). The wavelet-LHH-GLCM ClusterShade of the PET image selected to predict PLNM was slightly correlated with the corresponding feature of the IHC image (r=-0.165, p=0.045). There was a weak correlation of wavelet-LLL-GLRLM LongRunEmphasis of the PET image selected to predict COX-2 correlated with the corresponding feature of the IHC image (r=0.238, p=0.004). The correlation between PET image selected to predict COX-2 and the corresponding feature of the IHC image based on wavelet-LLL-GLRLM LongRunEmphasis is considered weak positive (r=0.238, p=0.004). Conclusions: This study underlined the significant application of the machine learning models based on PET/CT texture analysis for predicting PLNM and COX-2 expression, which could be a novel tool to assist the clinical management of cervical cancer with negative PLN on PET/CT images.