A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy

ObjectivesGliomas and brain metastases (Mets) are the most common brain malignancies. The treatment strategy and clinical prognosis of patients are different, requiring accurate diagnosis of tumor types. However, the traditional radiomics diagnostic pipeline requires manual annotation and lacks integrated methods for segmentation and classification. To improve the diagnosis process, a gliomas and Mets computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy on multi-center datasets was proposed.MethodsOverall, 1,022 high-grade gliomas and 775 Mets patients’ preoperative MR images were adopted in the study, including contrast-enhanced T1-weighted (T1-CE) and T2-fluid attenuated inversion recovery (T2-flair) sequences from three hospitals. Two segmentation models trained on the gliomas and Mets datasets, respectively, were used to automatically segment tumors. Multiple radiomics features were extracted after automatic segmentation. Several machine learning classifiers were used to measure the impact of feature selection methods. A weight soft voting (RSV) model and ensemble decision strategy based on prior knowledge (EDPK) were introduced in the radiomics pipeline. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the classification performance.ResultsThe proposed pipeline improved the diagnosis of gliomas and Mets with ACC reaching 0.8950 and AUC reaching 0.9585 after automatic lesion segmentation, which was higher than those of the traditional radiomics pipeline (ACC:0.8850, AUC:0.9450).ConclusionThe proposed model accurately classified gliomas and Mets patients using MRI radiomics. The novel pipeline showed great potential in diagnosing gliomas and Mets with high generalizability and interpretability

Quantitative MR Image Analysis - a Useful Tool in Differentiating Glioblastoma from Solitary Brain Metastasis

Cilj: Prikaz glioblastoma i metastaza na konvencionalnom MRI je često jako sličan, ali se terapijski pristup i prognoza bitno razlikuju. Čak i primenom naprednih MR tehnika, u nekim slučajevima dijagnoza ostaje nejasna. Glavni cilj disertacije bio je da utvrdi da li fraktalna ili teksturna, ili obe kvantitativne analize MR slike mogu doprineti diferencijaciji glioblastoma od solitarne metastaze mozga. Metod: Studija je sprovedena na ukupno 96 pacijenata sa dokazanim dijagnozama glioblastoma (50 pacijenata), odnosno solitarne metastaze (46 pacijenata). Izdvojene su slike sa najinformativnijim prikazom lezije (jedan isti presek u tri različite sekvence: CET1, T2 i SWI), a zatim je učinjena njihova kompjuterska analiza, primenom fraktalne metode brojanja kvadrata i teksturne metode bazirane na matrici zajedničke pojave istog nivoa sive boje (GLCM). Rezultati: Analizom sive skale celog tumora i binarne slike unutrašnjosti tumora sa T2 sekvence dobijen je najveći broj parametara koji značajno razlikuju dve vrste tumora (drugi ugaoni moment SASM, inverzni moment razlike SIDM, kontrast SCON, korelacija SCOR, diferencijalna fraktalna dimenzija DDIFF, odnosno binarna fraktalna dimenzija unutrašnjosti DBIN2, normirana fraktalna dimenzija DNORM, lakunarnost Ʌ2), dok su se druge dve sekvence (CET1 i SWI) pokazale manje pogodnim za kvantifikaciju. Kombinacijom parametara povećala se tačnost testiranja (AUC 0,838±0,041, senzitivnost 78% i specifičnost 76% za kombinaciju SASM i SIDM sa CET1 i T2 + SASM sa SWI + DBIN2 i DNORM sa T2). Zaključak: Kvantifikacija MR slike može doprineti diferencijalno dijagnostičkoj odluci između glioblastoma i solitarne metastaze mozga i potencijalno može postati deo svakodnevne radiološke prakse.Purpose: Presentation of glioblastomas and metastases on conventional MRI is quite similar, however treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of dissertation was to determine whether fractal, texture, or both quantitative MR image analysis could aid in differentiating glioblastoma from solitary brain metastasis. Method: Study embraced 96 patients with proven diagnosis of glioblastoma (50 patients), respectively solitary metastasis (46 patients). Images with the most representative lesion (one same slice on three different sequences: CET1, T2 and SWI) were selected, and computer analysis was done by fractal box-counting and texture gray level co-occurrence matrix (GLCM) methods. Results: Gray scale analysis of whole tumor and binary image analysis of tumor´s inner structures, both derived on T2 sequence, obtained the most significantly different parameters between two types of tumors (angular second moment SASM, inverse difference moment SIDM, contrast SCON, correlation SCOR, differential box dimension DDIFF, respectively binary box dimension DBIN2, normalized box dimension DNORM, lacunarity Ʌ2), while the other two sequences (CET1 and SWI) showed less suitable for quantification. The combinations of parameters yielded better results (AUC-0.838±0.041, sensitivity 78% and specificity 76% for next combination SASM and SIDM from CET1 and T2 + SASM from SWI + DBIN2 and DNORM from T2). Conclusions: MR image quantification may aid in differentiation between glioblastoma and solitary brain metastasis, and potentially could become a part of daily radiology practice