Dr. Mohammad M. Ghaemi Profile

Dr. Mohammad M. Ghaemi

at Jahad

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Publications (2)

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.

Proceedings Article | 4 April 2022 Presentation + Paper

Advanced survival prediction in head and neck cancer using hybrid machine learning systems and radiomics features

Mohammadreza Salmanpour, Mahdi Hosseinzadeh, Ensiyeh Modiri, Azizeh Akbari, Ghasem Hajianfar, Dariush Askari, Mehdi Fatan, Mehdi Maghsudi, Hanieh Ghaffari, Seyed Masoud Rezaeijo, Mohammad M. Ghaemi, Arman Rahmim

Proceedings Volume 12036, 1203614 (2022) https://doi.org/10.1117/12.2612816

KEYWORDS: Cancer, Positron emission tomography, Feature extraction, Machine learning, Feature selection, Discrete wavelet transforms, Computed tomography

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Accurate prognostic stratification of Head-and-Neck-Squamous-Cell-Carcinoma (HNSCC) patients can be an important clinical reference when designing therapeutic strategies. We set to predict 4 outcomes: overall survival (OS), distant metastasis (DM), locoregional recurrence (LR), and progression-free survival (SP). We studied Hybrid Machine Learning Systems (HMLS), applied to datasets with radiomics features. In this multicenter study, 408 HNSCC patients were extracted from The Cancer Imaging Archive (TCIA) database. PET images were registered to CT, enhanced, and cropped. 215 radiomics features were extracted from each region of interest via our standardized SERA radiomics package. We employed multiple HMLSs: 12 feature extraction (FEA) or 9 feature selection algorithms (FSA) linked with 9 survival-prediction-algorithms (SPA) optimized by 5-fold cross-validation, applied to PET only, CT only and 4 PET-CT datasets generated by image-level fusion strategies. Datasets were normalized by z-score-technique, and cindices were reported to compare the models. For OS prediction, the highest c-index 0.73 ± 0.10 was obtained for HMLS with Ratio of low-pass pyramid (RP) fusion technique + gaussian process latent variable model (GPLVM) + causal structure learning-based feature modification method (CSFM). For DM prediction, we achieved 0.80±0.06 via Dual-tree complex wavelet transform (DTCWT) fusion + Laplacian Score (LAP) + Logistic regression hazards (LH). For LR prediction, we arrived at a c-index of 0.73 ± 0.13 using PET + Sammon Mapping Algorithm (SM)+ deep neural network to distribute first hitting times (DHS). For SP prediction, the performance of 0.68 ± 0.02 was obtained via PET + SM + Relative risk model-depend on time (CoxTime). When no dimensionality reduction (FEA/FSA) was employed, the above 4 performances decreased to 0.69 ± 0. 10, 0.74 ± 0.13, 0.66 ± 0.15, and 0.68 ± 0.04 for OS, DM, LR and SP prediction. We demonstrated that using fusion techniques followed by appropriate HMLSs, including FEAs/FSAs and SPAs, improved prediction performance.

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