[Clinical Study on the Prognosis of Patients with Thymoma with Myasthenia Gravis]

Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences

The aim of this study was to develop a medical imaging and comprehensive stacked learning-based method for predicting high- and low-risk thymoma. A total of 126 patients with thymomas and 5 patients with thymic carcinoma treated at our institution, including 65 low-risk patients and 66 high-risk patients, were retrospectively recruited. Among them, 78 patients composed the training cohort, while the remaining 53 patients formed the validation cohort. We extracted 1702 features each from the patients' arterial-, venous-, and plain-phase images. Pairwise subtraction of these features yielded 1702 arterial-venous, arterial-plain, and venous-plain difference features each. The Mann‒Whitney U test and least absolute shrinkage and selection operator (LASSO) and SelectKBest methods were employed to select the best features from the training set. Six models were built with a stacked learning algorithm. By applying stacked ensemble learning, three machine learning algorithms (XGBoost, multilayer perceptron (MLP), and random forest) were combined by XGBoost to produce the the six basic imaging models. Then, the XGBoost algorithm was applied to the six basic imaging models to construct a combined radiomic model. Finally, the radiomic model was combined with clinical information to create a nomogram that could easily be used in clinical practice to predict the thymoma risk category. The areas under the curve (AUCs) of the combined radiomic model in the training and validation cohorts were 0.999 (95% CI 0.988-1.000) and 0.967 (95% CI 0.916-1.000), respectively, while those of the nomogram were 0.999 (95% CI 0.996-1.000) and 0.983 (95% CI 0.990-1.000). This study describes the application of CT-based radiomics in thymoma patients and proposes a nomogram for predicting the risk category for this disease, which could be advantageous for clinical decision-making for affected patients.

CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors

Objectives

This study aims to assess the performance of radiomics approaches based on 3D computed tomography (CT), clinical and semantic features in predicting the pathological classification of thymic epithelial tumors (TETs).

Methods

A total of 190 patients who underwent surgical resection and had pathologically confirmed TETs were enrolled in this retrospective study. All patients underwent non-contrast-enhanced CT (NECT) scans and contrast-enhanced CT (CECT) scans before treatment. A total of 396 hand-crafted radiomics features of each patient were extracted from the volume of interest in NECT and CECT images. We compared three clinical features and six semantic features (observed radiological traits) between patients with TETs. Two triple-classification radiomics models (RMs), two corresponding clinical RMs, and two corresponding clinical-semantic RMs were built to identify the types of the TETs. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were useful to evaluate the different models.

Results

Of the 190 patients, 83 had low-risk thymoma, 58 had high-risk thymoma, and 49 had thymic carcinoma. Clinical features (Age) and semantic features (mediastinal fat infiltration, mediastinal lymph node enlargement, and pleural effusion) were significantly different among the groups(P < 0.001). In the validation set, the NECT-based clinical RM (AUC = 0.770 for low-risk thymoma, 0.689 for high-risk thymoma, and 0.783 for thymic carcinoma; ACC = 0.569) performed better than the CECT-based clinical-semantic RM (AUC = 0.785 for low-risk thymoma, 0.576 for high-risk thymoma, and 0.774 for thymic carcinoma; ACC = 0.483).

Conclusions

NECT-based and CECT-based RMs may provide a non-invasive method to distinguish low-risk thymoma, high-risk thymoma, and thymic carcinoma, and NECT-based RMs performed better.

Advances in Knowledge

Radiomics models may be used for the preoperative prediction of the pathological classification of TETs.

Thymoma Recurrence and its Predisposing Factors in Iranian Population: a Single Center Study

BACKGROUND

Thymoma is relatively rare tumor. Prognosis and patients' outcome vary across different studies. We aimed to study the predisposing factors causing tumor recurrence in thymoma patients.

MATERIALS AND METHODS

A total of 43 thymoma or thymic carcinoma patients treated at the National Institute of Tuberculosis and Lung Disease (NRITLD), Masih Daneshvari Hospital from September 2005 to January 2017 were evaluated. The primary endpoint was the progression free survival (PFS). The relation of predisposing factors to PFS was studied.

RESULTS

Median age was 55 years old. The mean of follow-up duration was 22.9 months. The most prevalent pathology was thymoma unspecified. Pure red cell aplasia (n=3, 6.9%) was the most prevalent Para neoplastic syndrome. Most of the patients (n=23, 54%) were in stage III and IV Masaoka-Koga staging system. Disease progression was observed in 17 patients (39. 5%). Most recurrences occurred locally. None of demographic characteristics differed between patients who experienced disease recurrence and those who did not. After univariate and multivariate analysis, predisposing factor for disease progression was only Masaoka-Koga stage (P-value=0.015 and 0.031 respectively).

CONCLUSION

In this study, among different probable predisposing factors, only Masaoka-Koga stage had significant effect on disease recurrence. Large case-control studies may be required for better evaluation of risk factors.