Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models

Bos, Paula; Brekel, Michiel W. M. van den; Gouw, Zeno A.R.; Al‐Mamgani, Abrahim; Taghavi, Marjaneh; Waktola, Selam; Aerts, Hugo J.W.L.; Castelijns, Jonas A.; Jasperse, Bas

doi:10.1016/j.ejrad.2021.109701

Cited by 19 publications

(14 citation statements)

References 18 publications

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“…Many papers have demonstrated the prognostic values of clinical features for OPSCC such as HPV status, age, gender, T-stage, N-stage, and smoking status. 8,14,[36][37][38][39][40][41][42][43][44] They were also used as candidate predictors in our work. As shown in Figure 2A, high AUC values > 0.60 were obtained by clinical models for almost all outcome endpoints in the internal test set.…”

Section: Discussionmentioning

confidence: 99%

“…Many papers have demonstrated the prognostic values of clinical features for OPSCC such as HPV status, age, gender, T‐stage, N‐stage, and smoking status 8,14,36–44 . They were also used as candidate predictors in our work.…”

Section: Discussionmentioning

confidence: 99%

“…8 Prediction models based on clinical parameters and radiomics features have shown good performance in the prediction efficacy endpoints, such as overall survival (OS) and local control (LC), or complications like xerostomia in HNCs [9][10][11][12] and OPSCC patients. 13,14 However, these descriptive image features are restricted as they translate image characteristic in quantitative hand-crafted radiomics values. In contrast, convolutional neural networks (CNNs) can use information from the entire image, not compressing it to single values, and may therefore be more capable of obtaining the comprehensive and predictive information from the clinical images, and thus potentially improving OPSCC outcome predictions.…”

Section: Introductionmentioning

confidence: 99%

“…classified OPSCC patients as a low, intermediate, or high risk of death using HPV status, smoking combined with tumor stage, and nodal stage 8 . Prediction models based on clinical parameters and radiomics features have shown good performance in the prediction efficacy endpoints, such as overall survival (OS) and local control (LC), or complications like xerostomia in HNCs 9–12 and OPSCC patients 13,14 . However, these descriptive image features are restricted as they translate image characteristic in quantitative hand‐crafted radiomics values.…”

Section: Introductionmentioning

confidence: 99%

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CT‐based deep multi‐label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

Guo

Zhai

et al. 2023

Medical Physics

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BackgroundPersonalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high‐risk patients who may be suitable to receive de‐escalation or intensified treatment approaches.PurposeTo develop a deep learning (DL)‐based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT).MethodsTwo patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre‐treatment CT‐scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2‐year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis‐free survival (DMFS), disease‐specific survival (DSS), overall survival (OS), and disease‐free survival (DFS). We proposed DL outcome prediction models with the multi‐label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT‐scans.ResultsThe multi‐label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2‐year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2‐year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low‐risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set.ConclusionMLL models demonstrated better discriminative ability for all 2‐year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CT‐based deep multi‐label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

Guo

Zhai

et al. 2023

Medical Physics

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“…Sohn et al reported an AUC of 0.744 by constructing a least absolute shrinkage and selection operator (LASSO) logistic regression model based on radiomics features extracted from MRI images of 62 patients, and the results suggested that MRI radiomic phenotype can predict HPV status and be a potential imaging biomarker 13 . In addition to predicting HPV status, when a machine learning model was built using clinical factors and radiomic features, predictive models better predict loco‐regional recurrence after chemotherapy 14 . In this study, we attempted to create a predictive model based on MRI radiomics features for predicting important prognostic factors that are not available before surgery.…”

Section: Discussionmentioning

confidence: 99%

Machine learning and magnetic resonance imaging radiomics for predicting human papilloma virus status and prognostic factors in oropharyngeal squamous cell carcinoma

et al. 2022

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Background We attempted to predict pathological factors and treatment outcomes using machine learning and radiomic features extracted from preoperative magnetic resonance imaging (MRI) of oropharyngeal squamous cell carcinoma (OPSCC) patients. Methods The medical records and imaging data of 155 patients who were diagnosed with OPSCC were analyzed retrospectively. Results The logistic regression model showed that the area under the receiver operating characteristic curve (AUC) of the model was 0.792 in predicting human papilloma virus (HPV) status. The LightGBM model showed an AUC of 0.8333 in predicting HPV status. The performance of the logistic model in predicting lymphovascular invasion, extracapsular nodal spread, and metastatic lymph nodes showed AUC values of 0.7871, 0.6713, and 0.6638, respectively. In predicting disease recurrence, the LightGBM model showed an AUC of 0.8571. In predicting patient death, the logistic model showed an AUC of 0.8175. Conclusions A machine learning model using MRI radiomics showed satisfactory performance in predicting pathologic factors and treatment outcomes of OPSCC patients.

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Assessing the Performance of MRI-Radiomic Prognostic Signatures in Head and Neck Cancer Patients: A Comparative Analysis

Corti,

Mainardi,

Corino

2024

IFMBE Proceedings

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Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models

Cited by 19 publications

References 18 publications

CT‐based deep multi‐label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

CT‐based deep multi‐label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

Machine learning and magnetic resonance imaging radiomics for predicting human papilloma virus status and prognostic factors in oropharyngeal squamous cell carcinoma

Assessing the Performance of MRI-Radiomic Prognostic Signatures in Head and Neck Cancer Patients: A Comparative Analysis

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