Abstract
For the tumors located in the anterior skull base, germinoma and craniopharyngioma (CP) are unusual types with similar clinical manifestations and imaging features. The difference in treatment strategies and outcomes of patients highlights the importance of making an accurate preoperative diagnosis. This retrospective study enrolled 107 patients diagnosed with germinoma (n = 44) and CP (n = 63). The region of interest (ROI) was drawn independently by two researchers. Radiomic features were extracted from contrast-enhanced T1WI and T2WI sequences. Here, we established the diagnosis models with a combination of three selection methods, as well as three classifiers. After training the models, their performances were evaluated on the independent validation cohort and compared based on the index of the area under the receiver operating characteristic curve (AUC) in the validation cohort. Nine models were established and compared to find the optimal one defined with the highest AUC in the validation cohort. For the models applied in the contrast-enhanced T1WI images, RFS + RFC and LASSO + LDA were observed to be the optimal models with AUCs of 0.91. For the models applied in the T2WI images, DC + LDA and LASSO + LDA were observed to be the optimal models with AUCs of 0.88. The evidence of this study indicated that radiomics-based machine learning could be potentially considered as the radiological method in the presurgical differential diagnosis of germinoma and CP with a reliable diagnostic performance.
Highlights
Germ cell tumors (GCTs) mostly occur in pediatric and young adult patients [1].Germinoma is the most common subtype of GCTs, which accounted for approximately two-thirds of GCTs [2]
The main differential diagnosis of germinoma located in the anterior skull base is craniopharyngioma (CP), an intracranial tumor sharing similar clinical manifestations and imaging features with germinoma
We have narily demonstrated that the combination of machine learning algorithms and radiomic preliminarily demonstrated that the combination of machine learning algorithms and rafeatures extracted from MR images is helpful in the differential diagnosis of these two types diomic features extracted from MR images is helpful in the differential diagnosis of these of tumors, providing a new method to assist in conventional radiological diagnosis
Summary
Germ cell tumors (GCTs) mostly occur in pediatric and young adult patients [1].Germinoma is the most common subtype of GCTs, which accounted for approximately two-thirds of GCTs [2]. The main differential diagnosis of germinoma located in the anterior skull base is craniopharyngioma (CP), an intracranial tumor sharing similar clinical manifestations and imaging features with germinoma. Both of them are located in the suprasellar cistern [3,4], and dominated by non-specific symptoms of an elevated intracranial pressure symptom at the time of diagnosis, such as headache and nausea [4,5,6,7]. HCG is sometimes elevated in the serum or cerebrospinal fluid of patients with craniopharyngioma [11,12] In these cases, AFP and HCG cannot be applied as reliable biomarkers to differentiate between GCT and CP.
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