Mr. Kasra Mojallal Profile

Kasra Mojallal

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Proceedings Article | 4 April 2022 Poster + Paper

Prediction of TNM stage in head and neck cancer using hybrid machine learning systems and radiomics features

Mohammad Salmanpour, Mahdi Hosseinzadeh, Azizeh Akbari, Kasra Borazjani, Kasra Mojallal, Dariush Askari, Ghasem Hajianfar, Seyed Masoud Rezaeijo, Mohammad M. Ghaemi, Amir Hossein Nabizadeh, Arman Rahmim

Proceedings Volume 12033, 120332H (2022) https://doi.org/10.1117/12.2612998

KEYWORDS: Cancer, Positron emission tomography, Feature selection, Computed tomography, Machine learning, Evolutionary algorithms, Tumors, Feature extraction

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The tumor, node, metastasis (TNM) staging system enables clinicians to describe the spread of head-and-necksquamous-cell-carcinoma (HNSCC) cancer in a specific manner to assist with the assessment of disease status, prognosis, and management. This study aims to predict TNM staging for HNSCC cancer via Hybrid Machine Learning Systems (HMLSs) and radiomics features. In our study, 408 patients were included from the Cancer Imaging Archive (TCIA) database, included in a multi-center setting. PET images were registered to CT, enhanced, and cropped. We created 9 sets including CT-only, PET-only, and 7 PET-CT fusion sets. Next, 215 radiomics features were extracted from HNSCC tumor segmented by the physician via our standardized SERA radiomics package. We employed multiple HMLSs, including 16 feature-extraction (FEA) + 9 feature selection algorithms (FSA) linked with 8 classifiers optimized by grid-search approach, with model training, fine-tuning, and selection (5-fold cross-validation; 319 patients), followed by external-testing of selected model (89 patients). Datasets were normalized by z-score-technique, with accuracy reported to compare models. We first applied datasets with all features to classifiers only; accuracy of 0.69 ± 0.06 was achieved via PET applied to Random Forest classifier (RFC); performance of external testing (~ 0.62) confirmed our finding. Subsequently, we employed FSAs/FEAs prior to the application of classifiers. We achieved accuracy of 0.70 ± 0.03 for Curvelet transform (fusion) + Correlation-based Feature Selection (FSA) + K-Nearest Neighbor (classifier), and 0.70 ± 0.05 for PET + LASSO (FSA) + RFC (classifier). Accuracy of external testing (0.65 & 0.64) also confirmed these findings. Other HMLSs, applied on some fused datasets, also resulted in close performances. We demonstrate that classifiers or HMLSs linked with PET only and PET-CT fusion techniques enabled relatively low improved accuracy in predicting TNM stage. Meanwhile, the combination of PET and RFC enabled good prediction of TNM in HNSCC.

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