In this study, we proposed and validated a non-invasive method based on preoperative routine CT imaging of thymoma, referred to as “3D DenseNet deep learning (DL) based multi-model”, to detect TAMG before operation. With this model, we successfully filtered out most of TAMG patients in the training set (n = 182, AUC of 0.766), and further verified its reliability and efficacy in an external validation set (n = 48, AUC of 0.730). These results suggest our 3D-DenseNet-DL based multi-model is an effective and non-invasive method for screening TAMG in patients with thymoma. To our knowledge, this is the first study about the diagnosis of TAMG in thymoma patients by using machine learning based on CT imaging data.
Currently, there are three accepted diagnostic criteria for confirming MG by neurologists: immunological, electrophysiological, and pharmacological approaches. The immunological assay for serum AChR binding antibodies is considered as the most reliable approach to diagnose MG [24, 25]. AChR antibody is found in nearly all of TAMG patients, but the false positive rate was also high . Repetitive nerve stimulation (RNS)  and single-fiber electromyography (SFEMG)  are widely used in electrophysiological confirmation. However, SFEMG may not provide confirmation of the presence of MG unless weak muscles are tested, and the reliability of results is highly dependent on the experience of the technician . Pharmacological confirmation has long been used for the diagnosis of MG . However, the reported false-positive results  and the possible occurrence of potentially lethal vagal bradycardia following Tensilon injection , particularly in elderly persons, greatly limit its clinical application for MG confirmation. Therefore, although current diagnostic criteria are widely used for the final diagnosis of MG, some other methods may be used as a supplement for the initial screening or diagnosis of MG. Our 3D-DenseNet-DL based multi-model is a candidate, and the favorable results indicates that this model can be considered as a complementary method to the conventional diagnostic criteria, especially for screening TAMG before thoracic surgery. Considering the efficacy, safety, minimal-invasiveness and economic cost, we proposed a clinical flow chart for preoperative screening of MG: a combination of clinical symptoms, serum AChR antibody and image-based DL method (Figure S5). This flow chart may be important for necessary clinical management and preoperative risk assessment of the disease.
Nowadays, increasing number of studies are performed to evaluate the potential relationship between image and biological features of solid tumors , such as glioblastoma , rectal and lung adenocarcimoma[33, 34]. As the most common primary neoplasms of the mediastinum, the prediction of thymoma histology and stage by radiographic criteria have been mentioned in several previous reports. CT findings, such as smooth contours , calcification [35, 36], heterogeneous attenunation [36, 37], were interpreted as being of value in differentiating the various histologic subtypes of thymomas. Recently, Angelo lannarelli and colleagues  found the relationship between radiomics parameters, histology and grading of thymic tumors. More importantly, their study also demonstrated that MG syndrome was significantly associated with some parameters in quantitative texture analysis (QTA) , which represented an incentive for further evaluation the value of radiographic analysis in detection of MG syndrome in thymoma patients. Unfortunately, their study only included 16 patients (7 patients with TAMG). We therefore proposed a DL model based on preoperative CT imaging for screening TAMG in large cohort of thymoma patients (230 cases, and 95 with TAMG). Moreover, our results further confirmed the superior reliability and efficacy of this developed 3D-DenseNet-DL model comparing to the other five radiomic-based methods. These results also highlight the importance of radiographic analysis as diagnostic tools from the accurate characterization of the lesion itself to the detection of the paraneoplastic syndromes, which is a great stride in the application of AI in the medical field.
However, despite its satisfactory outcomes, this study has some limitations. First, given the retrospective nature of this analysis, a selection bias was unavoidable. Second, patients were not stratified into more detailed clinical status categories due to limited sample size. Third, the status of serum AChR binding antibodies was important for TAMG diagnosis, but the absence of such information in certain cases restrained further analysis. Therefore, a perspective, multi-center clinical trial with larger cohort would be indispensable to further confirm and optimize the screening model for MG patients.
In conclusion, with a large sample data for modeling and an independent cohort for external validation, we firstly developed a 3D-DenseNet-DL based multi-model for TAMG screening in thymoma patients based on preoperative CT imaging and achieved favorable results.