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Toosi A, Shiri I, Zaidi H, Rahmim A. Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs). Cancers (Basel) 2024; 16:2538. [PMID: 39061178 PMCID: PMC11274485 DOI: 10.3390/cancers16142538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method. The code for this work is publicly released.
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Affiliation(s)
- Amirhosein Toosi
- Department of Radiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada;
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Isaac Shiri
- Department of Cardiology, University Hospital Bern, CH-3010 Bern, Switzerland;
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland;
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland;
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada;
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Department of Physics & Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
- Department of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
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Lam V, O'Brien O, Amin O, Nigar E, Kumar M, Lingam RK. Oral cavity cancer and its pre-treatment radiological evaluation: A pictorial overview. Eur J Radiol 2024; 176:111494. [PMID: 38776803 DOI: 10.1016/j.ejrad.2024.111494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE Oral cavity cancer, primarily squamous cell carcinoma (SCC), is a prevalent malignancy globally, necessitating accurate clinical assessment and staging to enable effective treatment planning. Diagnosis requires biopsy and is followed by surgical resection and reconstruction as the primary therapeutic modality. Imaging plays a pivotal role during this process, aiding in the evaluation of tumour extent, nodal involvement and distant metastases. However, despite its value, both radiologists and clinicians must recognise its inherent limitations. METHODS This pictorial review article aims to illustrate the application of various imaging modalities in the pre-treatment evaluation of oral cavity SCC and highlights potential pitfalls. It underscores the importance of understanding the anatomical subsites of the oral cavity, the diverse patterns of spread tumours exhibit at each site, alongside the role of imaging in facilitating informed management strategies, while also acknowledging its limitations. RESULTS The review delves into fundamentals of current staging including nodal involvement, while, emphasising imaging strategies and potential limitations. Finally, it touches on the potential of novel radiomic techniques in characterising tumours and predicting treatment response. CONCLUSIONS Pre-treatment oral cavity cancer staging reflects an ongoing quest for enhanced diagnostic accuracy and prognostic prediction. Recognising the value of imaging alongside its limitations fosters a multidisciplinary approach to treatment planning, ultimately improving patient outcomes.
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Affiliation(s)
- Vincent Lam
- Department of Radiology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester LE1 5WW, United Kingdom
| | - Owen O'Brien
- Department of Radiology, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Road, London HA1 3UJ, United Kingdom
| | - Omed Amin
- Department of Radiology, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Road, London HA1 3UJ, United Kingdom; Department of Radiology, Chelsea and Westminster NHS Foundation Trust, 369 Fulham Rd, London SW10 9NH, United Kingdom
| | - Ezra Nigar
- Department of Pathology, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Road, London HA1 3UJ, United Kingdom
| | - Mahesh Kumar
- Department of Oral and Maxillofacial Surgery, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Road, London HA1 3UJ, United Kingdom; Department of Oral and Maxillofacial Surgery, Hillingdon Hospital, The Hillingdon Hospitals NHS Foundation Trust, Pield Heath Rd, Uxbridge UB8 3NN, United Kingdom
| | - Ravi Kumar Lingam
- Department of Radiology, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Road, London HA1 3UJ, United Kingdom.
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Huang YC, Huang SM, Yeh JH, Chang TC, Tsan DL, Lin CY, Tu SJ. Utility of CT Radiomics and Delta Radiomics for Survival Evaluation in Locally Advanced Nasopharyngeal Carcinoma with Concurrent Chemoradiotherapy. Diagnostics (Basel) 2024; 14:941. [PMID: 38732355 PMCID: PMC11083477 DOI: 10.3390/diagnostics14090941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/23/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of the TME concept. Both radiomic features and delta radiomic features were analyzed using CT images acquired in a routine radiotherapy process. METHODS A total of 50 patients with NPC stages III, IVA, and IVB were enrolled between September 2004 and February 2014. Survival models were built using Cox regression with clinical factors (i.e., gender, age, overall stage, T stage, N stage, and treatment dose) and radiomic features. Radiomic features were extracted from GTVt and GTVp. GTVp was created surrounding GTVt for TME consideration. Furthermore, delta radiomics, which is the longitudinal change in quantitative radiomic features, was utilized for analysis. Finally, C-index values were computed using leave-one-out cross-validation (LOOCV) to evaluate the performances of all prognosis models. RESULTS Models were built for three different clinical outcomes, including overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). The range of the C-index in clinical factor models was (0.622, 0.729). All radiomics models, including delta radiomics models, were in the range of (0.718, 0.872). Among delta radiomics models, GTVt and GTVp were in the range of (0.833, 0.872) and (0.799, 0.834), respectively. CONCLUSIONS Radiomic analysis on the proximal region surrounding the gross tumor volume of advanced NPC patients for survival outcome evaluation was investigated, and preliminary positive results were obtained. Radiomic models and delta radiomic models demonstrated performance that was either superior to or comparable with that of conventional clinical models.
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Affiliation(s)
- Yen-Cho Huang
- Department of Radiation Oncology, Keelung Chang Gung Memorial Hospital, Keelung 20445, Taiwan; (Y.-C.H.); (S.-M.H.); (J.-H.Y.)
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Tao-Yuan 33302, Taiwan
| | - Shih-Ming Huang
- Department of Radiation Oncology, Keelung Chang Gung Memorial Hospital, Keelung 20445, Taiwan; (Y.-C.H.); (S.-M.H.); (J.-H.Y.)
| | - Jih-Hsiang Yeh
- Department of Radiation Oncology, Keelung Chang Gung Memorial Hospital, Keelung 20445, Taiwan; (Y.-C.H.); (S.-M.H.); (J.-H.Y.)
| | - Tung-Chieh Chang
- Department of Radiation Oncology, Linkuo Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan; (T.-C.C.); (D.-L.T.)
| | - Din-Li Tsan
- Department of Radiation Oncology, Linkuo Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan; (T.-C.C.); (D.-L.T.)
| | - Chien-Yu Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Tao-Yuan 33302, Taiwan
- Department of Radiation Oncology, Linkuo Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan; (T.-C.C.); (D.-L.T.)
- Particle Physics and Beam Delivery Core Laboratory, Linkou Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
| | - Shu-Ju Tu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Tao-Yuan 33302, Taiwan
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
- Department of Nuclear Medicine, Linkou Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
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4
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Osapoetra LO, Dasgupta A, DiCenzo D, Fatima K, Quiaoit K, Saifuddin M, Karam I, Poon I, Husain Z, Tran WT, Sannachi L, Czarnota GJ. Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma. Radiol Imaging Cancer 2024; 6:e230029. [PMID: 38391311 PMCID: PMC10988345 DOI: 10.1148/rycan.230029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 11/24/2023] [Accepted: 01/17/2024] [Indexed: 02/24/2024]
Abstract
Purpose To investigate the role of quantitative US (QUS) radiomics data obtained after the 1st week of radiation therapy (RT) in predicting treatment response in individuals with head and neck squamous cell carcinoma (HNSCC). Materials and Methods This prospective study included 55 participants (21 with complete response [median age, 65 years {IQR: 47-80 years}, 20 male, one female; and 34 with incomplete response [median age, 59 years {IQR: 39-79 years}, 33 male, one female) with bulky node-positive HNSCC treated with curative-intent RT from January 2015 to October 2019. All participants received 70 Gy of radiation in 33-35 fractions over 6-7 weeks. US radiofrequency data from metastatic lymph nodes were acquired prior to and after 1 week of RT. QUS analysis resulted in five spectral maps from which mean values were extracted. We applied a gray-level co-occurrence matrix technique for textural analysis, leading to 20 QUS texture and 80 texture-derivative parameters. The response 3 months after RT was used as the end point. Model building and evaluation utilized nested leave-one-out cross-validation. Results Five delta (Δ) parameters had statistically significant differences (P < .05). The support vector machines classifier achieved a sensitivity of 71% (15 of 21), a specificity of 76% (26 of 34), a balanced accuracy of 74%, and an area under the receiver operating characteristic curve of 0.77 on the test set. For all the classifiers, the performance improved after the 1st week of treatment. Conclusion A QUS Δ-radiomics model using data obtained after the 1st week of RT from individuals with HNSCC predicted response 3 months after treatment completion with reasonable accuracy. Keywords: Computer-Aided Diagnosis (CAD), Ultrasound, Radiation Therapy/Oncology, Head/Neck, Radiomics, Quantitative US, Radiotherapy, Head and Neck Squamous Cell Carcinoma, Machine Learning Clinicaltrials.gov registration no. NCT03908684 Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
| | | | - Daniel DiCenzo
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Kashuf Fatima
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Karina Quiaoit
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Murtuza Saifuddin
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Irene Karam
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Ian Poon
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Zain Husain
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - William T. Tran
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Lakshmanan Sannachi
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
| | - Gregory J. Czarnota
- From the Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P.,
Z.H., W.T.T., G.J.C.), Medical Oncology (W.T.T.), and Medicine (W.T.T.),
Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON, Canada M4N
3M5; Departments of Radiation Oncology (L.O.O., A.D., I.K., I.P., Z.H., W.T.T.,
G.J.C.) and Medical Biophysics (G.J.C.), University of Toronto, Toronto, Canada;
and Departments of Physical Sciences (L.O.O., A.D., D.D., K.F., K.Q., M.S.,
L.S., G.J.C.) and Evaluative Clinical Sciences (W.T.T.), Sunnybrook Research
Institute, Toronto, Canada
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Agheli R, Siavashpour Z, Reiazi R, Azghandi S, Cheraghi S, Paydar R. Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach. Heliyon 2024; 10:e24866. [PMID: 38317933 PMCID: PMC10839875 DOI: 10.1016/j.heliyon.2024.e24866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 12/20/2023] [Accepted: 01/16/2024] [Indexed: 02/07/2024] Open
Abstract
Purpose To establish the early prediction models of radiation-induced oral mucositis (RIOM) based on baseline CT-based radiomic features (RFs), dosimetric data, and clinical features by machine learning models for head and neck cancer (HNC) patients. Methods In this single-center prospective study, 49 HNCs treated with curative intensity modulated radiotherapy (IMRT) were enrolled. Baseline CT images (i.e., CT simulation), dosimetric, and clinical features were collected. RIOM was assessed using CTCAE v.5.0. RFs were extracted from manually-contoured oral mucosa structures. Minimum-redundancy-maximum-relevance (mRMR) method was applied to select the most informative radiomics, dosimetric, and clinical features. Then, binary prediction models were constructed for predicting acute RIOM based on the top mRMR-ranked radiomics, dosimetric, and clinical features alone or in combination, using random forest classifier algorithm. The predictive performance of models was assessed using the area under the receiver operating curve (AUC), accuracy, weighted-average based sensitivity, precision, and F1-measure. Results Among extracted features, the top 10 RFs, the top 5 dose-volume features, and the top 5 clinical features were selected using mRMR method. The model exploiting the integrated features (10-radiomics + 5-dosimetric + 5-clinical) achieved the best prediction with AUC, accuracy, sensitivity, precision, and F1-measure values of 91.7 %, 90.0 %, 83.0 % 100.0 %, and 91.0 %, respectively. The model developed using baseline CT RFs alone provided the best performance compared to dose-volume features or clinical features alone, with an AUC of 87.0 %. Conclusion Our results suggest that the integration of baseline CT radiomic features with dosimetric and clinical features showed promising potential to improve the performance of machine learning models in early prediction of RIOM. The ultimate goal is to personalize radiotherapy for HNC patients.
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Affiliation(s)
- Razieh Agheli
- Radiation Sciences Department, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Zahra Siavashpour
- Department of Radiation Oncology, Shohada-e Tajrish Educational Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Reiazi
- Department of Radiation Physics, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Samira Azghandi
- Department of Radiation Oncology, Shohada-e Tajrish Educational Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Susan Cheraghi
- Radiation Sciences Department, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Reza Paydar
- Radiation Sciences Department, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
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6
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Chen Z, Yu Y, Liu S, Du W, Hu L, Wang C, Li J, Liu J, Zhang W, Peng X. A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma. Clin Oral Investig 2023; 28:39. [PMID: 38151672 DOI: 10.1007/s00784-023-05423-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/21/2023] [Indexed: 12/29/2023]
Abstract
OBJECTIVES In this study, we constructed and validated models based on deep learning and radiomics to facilitate preoperative diagnosis of cervical lymph node metastasis (LNM) using contrast-enhanced computed tomography (CECT). MATERIALS AND METHODS CECT scans of 100 patients with OSCC (217 metastatic and 1973 non-metastatic cervical lymph nodes: development set, 76 patients; internally independent test set, 24 patients) who received treatment at the Peking University School and Hospital of Stomatology between 2012 and 2016 were retrospectively collected. Clinical diagnoses and pathological findings were used to establish the gold standard for metastatic cervical LNs. A reader study with two clinicians was also performed to evaluate the lymph node status in the test set. The performance of the proposed models and the clinicians was evaluated and compared by measuring using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). RESULTS A fusion model combining deep learning with radiomics showed the best performance (ACC, 89.2%; SEN, 92.0%; SPE, 88.9%; and AUC, 0.950 [95% confidence interval: 0.908-0.993, P < 0.001]) in the test set. In comparison with the clinicians, the fusion model showed higher sensitivity (92.0 vs. 72.0% and 60.0%) but lower specificity (88.9 vs. 97.5% and 98.8%). CONCLUSION A fusion model combining radiomics and deep learning approaches outperformed other single-technique models and showed great potential to accurately predict cervical LNM in patients with OSCC. CLINICAL RELEVANCE The fusion model can complement the preoperative identification of LNM of OSCC performed by the clinicians.
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Affiliation(s)
- Zhen Chen
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Yao Yu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Shuo Liu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Wen Du
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Leihao Hu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Congwei Wang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Jiaqi Li
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Jianbo Liu
- Huafang Hanying Medical Technology Co., Ltd, No.19, West Bridge Road, Miyun District, Beijing, 101520, People's Republic of China
| | - Wenbo Zhang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China
| | - Xin Peng
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China.
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7
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Andrearczyk V, Oreiller V, Boughdad S, Le Rest CC, Tankyevych O, Elhalawani H, Jreige M, Prior JO, Vallières M, Visvikis D, Hatt M, Depeursinge A. Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge. Med Image Anal 2023; 90:102972. [PMID: 37742374 DOI: 10.1016/j.media.2023.102972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 07/27/2023] [Accepted: 09/14/2023] [Indexed: 09/26/2023]
Abstract
By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables. This is especially true in Head and Neck Cancer (HNC). The goal of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge was to develop and compare modern image analysis methods to best extract and leverage this information automatically. We present here the post-analysis of HECKTOR 2nd edition, at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021. The scope of the challenge was substantially expanded compared to the first edition, by providing a larger population (adding patients from a new clinical center) and proposing an additional task to the challengers, namely the prediction of Progression-Free Survival (PFS). To this end, the participants were given access to a training set of 224 cases from 5 different centers, each with a pre-treatment FDG-PET/CT scan and clinical variables. Their methods were subsequently evaluated on a held-out test set of 101 cases from two centers. For the segmentation task (Task 1), the ranking was based on a Borda counting of their ranks according to two metrics: mean Dice Similarity Coefficient (DSC) and median Hausdorff Distance at 95th percentile (HD95). For the PFS prediction task, challengers could use the tumor contours provided by experts (Task 3) or rely on their own (Task 2). The ranking was obtained according to the Concordance index (C-index) calculated on the predicted risk scores. A total of 103 teams registered for the challenge, for a total of 448 submissions and 29 papers. The best method in the segmentation task obtained an average DSC of 0.759, and the best predictions of PFS obtained a C-index of 0.717 (without relying on the provided contours) and 0.698 (using the expert contours). An interesting finding was that best PFS predictions were reached by relying on DL approaches (with or without explicit tumor segmentation, 4 out of the 5 best ranked) compared to standard radiomics methods using handcrafted features extracted from delineated tumors, and by exploiting alternative tumor contours (automated and/or larger volumes encompassing surrounding tissues) rather than relying on the expert contours. This second edition of the challenge confirmed the promising performance of fully automated primary tumor delineation in PET/CT images of HNC patients, although there is still a margin for improvement in some difficult cases. For the first time, the prediction of outcome was also addressed and the best methods reached relatively good performance (C-index above 0.7). Both results constitute another step forward toward large-scale outcome prediction studies in HNC.
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Affiliation(s)
- Vincent Andrearczyk
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland.
| | - Valentin Oreiller
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Sarah Boughdad
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Catherine Cheze Le Rest
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France; Poitiers University Hospital, nuclear medicine, Poitiers, France
| | - Olena Tankyevych
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France; Poitiers University Hospital, nuclear medicine, Poitiers, France
| | - Hesham Elhalawani
- Cleveland Clinic Foundation, Department of Radiation Oncology, Cleveland, OH, United States of America
| | - Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | | | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | - Adrien Depeursinge
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
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8
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Chang YS, Nair JR, McDougall CC, Qiu W, Banerjee R, Joshi M, Lysack JT. Risk Stratification for Oropharyngeal Squamous Cell Carcinoma Using Texture Analysis on CT - A Step Beyond HPV Status. Can Assoc Radiol J 2023; 74:657-666. [PMID: 36856197 DOI: 10.1177/08465371231157592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
Abstract
Background and Purpose: Human papillomavirus-associated oropharyngeal squamous cell carcinoma (OPSCC) is increasingly prevalent. Despite the overall more favorable outcome, the observed heterogeneous treatment response within this patient group highlights the need for additional means to prognosticate and guide clinical decision-making. Promising prediction models using radiomics from primary OPSCC have been derived. However, no model/s using metastatic lymphadenopathy exist to allow prognostication in those instances when the primary tumor is not seen. The aim of our study was to evaluate whether radiomics using metastatic lymphadenopathy allows for the development of a useful risk assessment model comparable to the primary tumor and whether additional knowledge of the HPV status further improves its prognostic efficacy. Materials and Methods: 80 consecutive patients diagnosed with stage III-IV OPSCC between February 2009 and October 2015, known human papillomavirus status, and pre-treatment CT images were retrospectively identified. Manual segmentation of primary tumor and metastatic lymphadenopathy was performed and the extracted texture features were used to develop multivariate assessment models to prognosticate treatment response. Results: Texture analysis of either the primary or metastatic lymphadenopathy from pre-treatment enhanced CT images can be used to develop models for the stratification of treatment outcomes in OPSCC patients. AUCs range from .78 to .85 for the various OPSCC groups tested, indicating high predictive capability of the models. Conclusions: This preliminary study can form the basis multi-centre trial that may help optimize treatment and improve quality of life in patients with OPSCC in the era of personalized medicine.
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Affiliation(s)
- Yuh-Shin Chang
- Division of Neuroradiology, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
| | - Jaykumar Raghavan Nair
- Division of Neuroradiology, University of Calgary, Calgary, AB, Canada
- Department of Radiology, QEII Health Science Centre, Halifax Infirmary Hospital, Dalhousie University, Halifax, NS, Canada
| | - Connor C McDougall
- Department of Mechanical Engineering, University of Calgary, Calgary, AB, Canada
| | - Wu Qiu
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Robyn Banerjee
- Division of Radiation Oncology, University of Calgary, Calgary, AB, Canada
| | - Manish Joshi
- Division of Neuroradiology, University of Calgary, Calgary, AB, Canada
| | - John T Lysack
- Division of Neuroradiology, University of Calgary, Calgary, AB, Canada
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9
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Tu CH, Wang RT, Wang BS, Kuo CE, Wang EY, Tu CT, Yu WN. Neural network combining with clinical ultrasonography: A new approach for classification of salivary gland tumors. Head Neck 2023; 45:1885-1893. [PMID: 37222027 DOI: 10.1002/hed.27396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/21/2023] [Accepted: 04/28/2023] [Indexed: 05/25/2023] Open
Abstract
OBJECTIVE Little information is available about deep learning methods used in ultrasound images of salivary gland tumors. We aimed to compare the accuracy of the ultrasound-trained model to computed tomography or magnetic resonance imaging trained model. MATERIALS AND METHODS Six hundred and thirty-eight patients were included in this retrospective study. There were 558 benign and 80 malignant salivary gland tumors. A total of 500 images (250 benign and 250 malignant) were acquired in the training and validation set, then 62 images (31 benign and 31 malignant) in the test set. Both machine learning and deep learning were used in our model. RESULTS The test accuracy, sensitivity, and specificity of our final model were 93.5%, 100%, and 87%, respectively. There were no over fitting in our model as the validation accuracy was similar with the test accuracy. CONCLUSIONS The sensitivity and specificity were comparable with current MRI and CT images using artificial intelligence.
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Affiliation(s)
- Cheng-Hung Tu
- Department of Otorhinolaryngology - Head and Neck Surgery, Changhua Christian Hospital, Changhua, Taiwan
| | - Rui-Teng Wang
- Department of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan
| | - Bo-Sen Wang
- Department of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan
| | - Chih-En Kuo
- Department of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan
| | - En-Ying Wang
- Department of Otorhinolaryngology - Head and Neck Surgery, Changhua Christian Hospital, Changhua, Taiwan
| | - Ching-Ting Tu
- Department of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan
| | - Wan-Nien Yu
- Department of Otorhinolaryngology - Head and Neck Surgery, Changhua Christian Hospital, Changhua, Taiwan
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10
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Kazmierski M, Welch M, Kim S, McIntosh C, Rey-McIntyre K, Huang SH, Patel T, Tadic T, Milosevic M, Liu FF, Ryczkowski A, Kazmierska J, Ye Z, Plana D, Aerts HJ, Kann BH, Bratman SV, Hope AJ, Haibe-Kains B. Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics. CANCER RESEARCH COMMUNICATIONS 2023; 3:1140-1151. [PMID: 37397861 PMCID: PMC10309070 DOI: 10.1158/2767-9764.crc-22-0152] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 11/14/2022] [Accepted: 05/19/2023] [Indexed: 07/04/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) are becoming critical in developing and deploying personalized medicine and targeted clinical trials. Recent advances in ML have enabled the integration of wider ranges of data including both medical records and imaging (radiomics). However, the development of prognostic models is complex as no modeling strategy is universally superior to others and validation of developed models requires large and diverse datasets to demonstrate that prognostic models developed (regardless of method) from one dataset are applicable to other datasets both internally and externally. Using a retrospective dataset of 2,552 patients from a single institution and a strict evaluation framework that included external validation on three external patient cohorts (873 patients), we crowdsourced the development of ML models to predict overall survival in head and neck cancer (HNC) using electronic medical records (EMR) and pretreatment radiological images. To assess the relative contributions of radiomics in predicting HNC prognosis, we compared 12 different models using imaging and/or EMR data. The model with the highest accuracy used multitask learning on clinical data and tumor volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction, outperforming models relying on clinical data only, engineered radiomics, or complex deep neural network architecture. However, when we attempted to extend the best performing models from this large training dataset to other institutions, we observed significant reductions in the performance of the model in those datasets, highlighting the importance of detailed population-based reporting for AI/ML model utility and stronger validation frameworks. We have developed highly prognostic models for overall survival in HNC using EMRs and pretreatment radiological images based on a large, retrospective dataset of 2,552 patients from our institution.Diverse ML approaches were used by independent investigators. The model with the highest accuracy used multitask learning on clinical data and tumor volume.External validation of the top three performing models on three datasets (873 patients) with significant differences in the distributions of clinical and demographic variables demonstrated significant decreases in model performance. Significance ML combined with simple prognostic factors outperformed multiple advanced CT radiomics and deep learning methods. ML models provided diverse solutions for prognosis of patients with HNC but their prognostic value is affected by differences in patient populations and require extensive validation.
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Affiliation(s)
- Michal Kazmierski
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Mattea Welch
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- TECHNA Institute, Toronto, Ontario, Canada
| | - Sejin Kim
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Chris McIntosh
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- TECHNA Institute, Toronto, Ontario, Canada
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Katrina Rey-McIntyre
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Shao Hui Huang
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Ontario, Canada
| | - Tirth Patel
- TECHNA Institute, Toronto, Ontario, Canada
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Tony Tadic
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Ontario, Canada
| | - Michael Milosevic
- TECHNA Institute, Toronto, Ontario, Canada
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Ontario, Canada
| | - Fei-Fei Liu
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Ontario, Canada
| | - Adam Ryczkowski
- Department of Medical Physics, Greater Poland Cancer Centre, Poznan, Poland
- Department of Electroradiology, University of Medical Sciences, Poznan, Poland
| | - Joanna Kazmierska
- Department of Electroradiology, University of Medical Sciences, Poznan, Poland
- Department of Radiotherapy II, Greater Poland Cancer Centre, Poznan, Poland
| | - Zezhong Ye
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute / Brigham and Women's Hosptial, Boston, Massachusetts
| | - Deborah Plana
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute / Brigham and Women's Hosptial, Boston, Massachusetts
| | - Hugo J.W.L. Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute / Brigham and Women's Hosptial, Boston, Massachusetts
- Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands
| | - Benjamin H. Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts
- Department of Radiation Oncology, Dana-Farber Cancer Institute / Brigham and Women's Hosptial, Boston, Massachusetts
| | - Scott V. Bratman
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Ontario, Canada
| | - Andrew J. Hope
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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11
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Wang K, Dohopolski M, Zhang Q, Sher D, Wang J. Towards reliable head and neck cancers locoregional recurrence prediction using delta-radiomics and learning with rejection option. Med Phys 2023; 50:2212-2223. [PMID: 36484346 PMCID: PMC10121744 DOI: 10.1002/mp.16132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/08/2022] [Accepted: 11/20/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE A reliable locoregional recurrence (LRR) prediction model is important for the personalized management of head and neck cancers (HNC) patients who received radiotherapy. This work aims to develop a delta-radiomics feature-based multi-classifier, multi-objective, and multi-modality (Delta-mCOM) model for post-treatment HNC LRR prediction. Furthermore, we aim to adopt a learning with rejection option (LRO) strategy to boost the reliability of Delta-mCOM model by rejecting prediction for samples with high prediction uncertainties. METHODS In this retrospective study, we collected PET/CT image and clinical data from 224 HNC patients who received radiotherapy (RT) at our institution. We calculated the differences between radiomics features extracted from PET/CT images acquired before and after radiotherapy and used them in conjunction with pre-treatment radiomics features as the input features. Using clinical parameters, PET radiomics features, and CT radiomics features, we built and optimized three separate single-modality models. We used multiple classifiers for model construction and employed sensitivity and specificity simultaneously as the training objectives for each of them. Then, for testing samples, we fused the output probabilities from all these single-modality models to obtain the final output probabilities of the Delta-mCOM model. In the LRO strategy, we estimated the epistemic and aleatoric uncertainties when predicting with a trained Delta-mCOM model and identified patients associated with prediction of higher reliability (low uncertainty estimates). The epistemic and aleatoric uncertainties were estimated using an AutoEncoder-style anomaly detection model and test-time augmentation (TTA) with predictions made from the Delta-mCOM model, respectively. Predictions with higher epistemic uncertainty or higher aleatoric uncertainty than given thresholds were deemed unreliable, and they were rejected before providing a final prediction. In this study, different thresholds corresponding to different low-reliability prediction rejection ratios were applied. Their values are based on the estimated epistemic and aleatoric uncertainties distribution of the validation data. RESULTS The Delta-mCOM model performed significantly better than the single-modality models, whether trained with pre-, post-treatment radiomics features or concatenated BaseLine and Delta-Radiomics Features (BL-DRFs). It was numerically superior to the PET and CT fused BL-DRF model (nonstatistically significant). Using the LRO strategy for the Delta-mCOM model, most of the evaluation metrics improved as the rejection ratio increased from 0% to around 25%. Utilizing both epistemic and aleatoric uncertainty for rejection yielded nonstatistically significant improved metrics compared to each alone at approximately a 25% rejection ratio. Metrics were significantly better than the no-rejection method when the reject ratio was higher than 50%. CONCLUSIONS The inclusion of the delta-radiomics feature improved the accuracy of HNC LRR prediction, and the proposed Delta-mCOM model can give more reliable predictions by rejecting predictions for samples of high uncertainty using the LRO strategy.
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Affiliation(s)
- Kai Wang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Michael Dohopolski
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Qiongwen Zhang
- Department of Head and Neck Oncology, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - David Sher
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jing Wang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
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12
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Varghese AJ, Gouthamchand V, Sasidharan BK, Wee L, Sidhique SK, Rao JP, Dekker A, Hoebers F, Devakumar D, Irodi A, Balasingh TP, Godson HF, Joel T, Mathew M, Gunasingam Isiah R, Pavamani SP, Thomas HMT. Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization. Phys Imaging Radiat Oncol 2023; 26:100450. [PMID: 37260438 PMCID: PMC10227455 DOI: 10.1016/j.phro.2023.100450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 06/02/2023] Open
Abstract
Background and purpose Radiomics models trained with limited single institution data are often not reproducible and generalisable. We developed radiomics models that predict loco-regional recurrence within two years of radiotherapy with private and public datasets and their combinations, to simulate small and multi-institutional studies and study the responsiveness of the models to feature selection, machine learning algorithms, centre-effect harmonization and increased dataset sizes. Materials and methods 562 patients histologically confirmed and treated for locally advanced head-and-neck cancer (LA-HNC) from two public and two private datasets; one private dataset exclusively reserved for validation. Clinical contours of primary tumours were not recontoured and were used for Pyradiomics based feature extraction. ComBat harmonization was applied, and LASSO-Logistic Regression (LR) and Support Vector Machine (SVM) models were built. 95% confidence interval (CI) of 1000 bootstrapped area-under-the-Receiver-operating-curves (AUC) provided predictive performance. Responsiveness of the models' performance to the choice of feature selection methods, ComBat harmonization, machine learning classifier, single and pooled data was evaluated. Results LASSO and SelectKBest selected 14 and 16 features, respectively; three were overlapping. Without ComBat, the LR and SVM models for three institutional data showed AUCs (CI) of 0.513 (0.481-0.559) and 0.632 (0.586-0.665), respectively. Performances following ComBat revealed AUCs of 0.559 (0.536-0.590) and 0.662 (0.606-0.690), respectively. Compared to single cohort AUCs (0.562-0.629), SVM models from pooled data performed significantly better at AUC = 0.680. Conclusions Multi-institutional retrospective data accentuates the existing variabilities that affect radiomics. Carefully designed prospective, multi-institutional studies and data sharing are necessary for clinically relevant head-and-neck cancer prognostication models.
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Affiliation(s)
- Amal Joseph Varghese
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Varsha Gouthamchand
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sharief K Sidhique
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | | | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Devadhas Devakumar
- Department of Nuclear Medicine, Christian Medical College, Vellore, Tamil Nadu, India
| | - Aparna Irodi
- Department of Radiology, Christian Medical College, Vellore, Tamil Nadu, India
| | | | - Henry Finlay Godson
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | - T Joel
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Manu Mathew
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | | | | | - Hannah Mary T Thomas
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
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13
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Bologna M, Corino V, Cavalieri S, Calareso G, Gazzani SE, Poli T, Ravanelli M, Mattavelli D, de Graaf P, Nauta I, Scheckenbach K, Licitra L, Mainardi L. Prognostic radiomic signature for head and neck cancer: development and validation on a multi-centric MRI dataset. Radiother Oncol 2023; 183:109638. [PMID: 37004837 DOI: 10.1016/j.radonc.2023.109638] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 03/10/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
BACKGROUND AND PURPOSE Prognosis in locally advanced head and neck cancer (HNC) is currently based on TNM staging system and tumor subsite. However, quantitative imaging features (i.e., radiomic features) from magnetic resonance imaging (MRI) may provide additional prognostic info. The aim of this work is to develop and validate an MRI-based prognostic radiomic signature for locally advanced HNC. MATERIALS AND METHODS Radiomic features were extracted from T1- and T2-weighted MRI (T1w and T2w) using the segmentation of the primary tumor as mask. In total 1072 features (536 per image type) were extracted for each tumor. A retrospective multi-centric dataset (n=285) was used for features selection and model training. The selected features were used to fit a Cox proportional hazard regression model for overall survival (OS) that outputs the radiomic signature. The signature was then validated on a prospective multi-centric dataset (n=234). Prognostic performance for OS and disease-free survival (DFS) was evaluated using C-index. Additional prognostic value of the radiomic signature was explored. RESULTS The radiomic signature had C-index=0.64 for OS and C-index=0.60 for DFS in the validation set. The addition of the radiomic signature to other clinical features (TNM staging and tumor subsite) increased prognostic ability for both OS (HPV- C-index 0.63 to 0.65; HPV+ C-index 0.75 to 0.80) and DFS (HPV- C-index 0.58 to 0.61; HPV+ C-index 0.64 to 0.65). CONCLUSION An MRI-based prognostic radiomic signature was developed and prospectively validated. Such signature can successfully integrate clinical factors in both HPV+ and HPV- tumors. Grant support: European Union Horizon 2020 Framework Programme, Grant/Award, Number: 689715.
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Affiliation(s)
- Marco Bologna
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy.
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Silvia Eleonora Gazzani
- Unit of Diagnostic and Interventional Radiology, Department of Surgical Sciences, University of Parma, Parma, Italy
| | - Tito Poli
- Unit of Maxillo-Facial Surgery, Department of Biomedical, Biotechnological and Translational Sciences (S.Bi.Bi.T.), University of Parma, Parma, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Davide Mattavelli
- Department of Otorhinolaryngology Head and Neck Surgery, Spedali Civili di Brescia and University of Brescia, Brescia, Italy
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Irene Nauta
- Department of Otolaryngology/Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, The Netherlands
| | - Kathrin Scheckenbach
- Department of Otolaryngology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan. Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
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14
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Zhao X, Li W, Zhang J, Tian S, Zhou Y, Xu X, Hu H, Lei D, Wu F. Radiomics analysis of CT imaging improves preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma. Eur Radiol 2023; 33:1121-1131. [PMID: 35984515 DOI: 10.1007/s00330-022-09051-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/16/2022] [Accepted: 07/23/2022] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To investigate the role of CT radiomics for preoperative prediction of lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC). METHODS LSCC patients who received open surgery and lymphadenectomy were enrolled and randomized into primary and validation cohorts at a ratio of 7:3 (325 vs. 139). In the primary cohort, we extracted radiomics features from whole intratumoral regions on venous-phase CT images and constructed a radiomics signature by least absolute shrinkage and selection operator (LASSO) regression. A radiomics model incorporating the radiomic signature and independent clinical factors was established via multivariable logistic regression and presented as a nomogram. Nomogram performance was compared with a clinical model and traditional CT report with respect to its discrimination and clinical usefulness. The radiomics nomogram was internally tested in an independent validation cohort. RESULTS The radiomics signature, composed of 9 stable features, was associated with LNM in both the primary and validation cohorts (both p < .001). A radiomics model incorporating independent predictors of LNM (the radiomics signature, tumor subsite, and CT report) showed significantly better discrimination of nodal status than either the clinical model or the CT report in the primary cohort (AUC 0.91 vs. 0.84 vs. 0.68) and validation cohort (AUC 0.89 vs. 0.83 vs. 0.70). Decision curve analysis confirmed that the radiomics nomogram was superior to the clinical model and traditional CT report. CONCLUSIONS The CT-based radiomics nomogram may improve preoperative identification of nodal status and help in clinical decision-making in LSCC. KEY POINTS • The radiomics model showed favorable performance for predicting LN metastasis in LSCC patients. • The radiomics model may help in clinical decision-making and define patient subsets benefiting most from neck treatment.
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Affiliation(s)
- Xingguo Zhao
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Wenming Li
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, 250012, Shandong, China
| | - Jiulou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shui Tian
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yang Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Xiaoquan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Hao Hu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Dapeng Lei
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, 250012, Shandong, China.
| | - Feiyun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
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15
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Gangil T, Sharan K, Rao BD, Palanisamy K, Chakrabarti B, Kadavigere R. Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning. PLoS One 2022; 17:e0277168. [PMID: 36520945 PMCID: PMC9754241 DOI: 10.1371/journal.pone.0277168] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 10/24/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Radiomics involves the extraction of quantitative information from annotated Computed-Tomography (CT) images, and has been used to predict outcomes in Head and Neck Squamous Cell Carcinoma (HNSCC). Subjecting combined Radiomics and Clinical features to Machine Learning (ML) could offer better predictions of clinical outcomes. This study is a comparative performance analysis of ML models with Clinical, Radiomics, and Clinico-Radiomic datasets for predicting four outcomes of HNSCC treated with Curative Radiation Therapy (RT): Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease. METHODOLOGY The study used retrospective data of 311 HNSCC patients treated with radiotherapy between 2013-2018 at our centre. Binary prediction models were developed for the four outcomes with Clinical-only, Clinico-Radiomic, and Radiomics-only datasets, using three different ML classification algorithms namely, Random Forest (RF), Kernel Support Vector Machine (KSVM), and XGBoost. The best-performing ML algorithms of the three dataset groups was then compared. RESULTS The Clinico-Radiomic dataset using KSVM classifier provided the best prediction. Predicted mean testing accuracy for Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease was 97%, 72%, 99%, and 96%, respectively. The mean area under the receiver operating curve (AUC) was calculated and displayed for all the models using three dataset groups. CONCLUSION Clinico-Radiomic dataset improved the predictive ability of ML models over clinical features alone, while models built using Radiomics performed poorly. Radiomics data could therefore effectively supplement clinical data in predicting outcomes.
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Affiliation(s)
- Tarun Gangil
- Department of Radiotherapy and Oncology, Kasturba Medical College-Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Krishna Sharan
- Department of Radiotherapy and Oncology, Kasturba Medical College-Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - B. Dinesh Rao
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | | | | | - Rajagopal Kadavigere
- Department of Radiology, Kasturba Medical College-Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- * E-mail:
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16
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Fathi Kazerooni A, Nabil M, Alviri M, Koopaei S, Salahshour F, Assili S, Saligheh Rad H, Aghaghazvini L. Radiomic Analysis of Multi-parametric MR Images (MRI) for Classification of Parotid Tumors. J Biomed Phys Eng 2022; 12:599-610. [PMID: 36569565 PMCID: PMC9759641 DOI: 10.31661/jbpe.v0i0.2007-1140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 11/13/2020] [Indexed: 12/05/2022]
Abstract
Background Characterization of parotid tumors before surgery using multi-parametric magnetic resonance imaging (MRI) scans can support clinical decision making about the best-suited therapeutic strategy for each patient. Objective This study aims to differentiate benign from malignant parotid tumors through radiomics analysis of multi-parametric MR images, incorporating T2-w images with ADC-map and parametric maps generated from Dynamic Contrast Enhanced MRI (DCE-MRI). Material and Methods MRI scans of 31 patients with histopathologically-confirmed parotid gland tumors (23 benign, 8 malignant) were included in this retrospective study. For DCE-MRI, semi-quantitative analysis, Tofts pharmacokinetic (PK) modeling, and five-parameter sigmoid modeling were performed and parametric maps were generated. For each patient, borders of the tumors were delineated on whole tumor slices of T2-w image, ADC-map, and the late-enhancement dynamic series of DCE-MRI, creating regions-of-interest (ROIs). Radiomic analysis was performed for the specified ROIs. Results Among the DCE-MRI-derived parametric maps, wash-in rate (WIR) and PK-derived Ktrans parameters surpassed the accuracy of other parameters based on support vector machine (SVM) classifier. Radiomics analysis of ADC-map outperformed the T2-w and DCE-MRI techniques using the simpler classifier, suggestive of its inherently high sensitivity and specificity. Radiomics analysis of the combination of T2-w image, ADC-map, and DCE-MRI parametric maps resulted in accuracy of 100% with both classifiers with fewer numbers of selected texture features than individual images. Conclusion In conclusion, radiomics analysis is a reliable quantitative approach for discrimination of parotid tumors and can be employed as a computer-aided approach for pre-operative diagnosis and treatment planning of the patients.
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Affiliation(s)
- Anahita Fathi Kazerooni
- PhD, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
| | - Mahnaz Nabil
- PhD, Department of Mathematics, Islamic Azad University, Qazvin Branch, Qazvin, Iran
| | - Mohammadreza Alviri
- MSc, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
| | - Soheila Koopaei
- MSc, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
| | - Faeze Salahshour
- MD, Department of Radiology, Advanced Diagnostic and Invasive Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Sanam Assili
- MSc, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
| | - Hamidreza Saligheh Rad
- PhD, Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Iran
| | - Leila Aghaghazvini
- MD, Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
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17
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Gonçalves M, Gsaxner C, Ferreira A, Li J, Puladi B, Kleesiek J, Egger J, Alves V. Radiomics in Head and Neck Cancer Outcome Predictions. Diagnostics (Basel) 2022; 12:2733. [PMID: 36359576 PMCID: PMC9689406 DOI: 10.3390/diagnostics12112733] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/02/2022] [Accepted: 11/04/2022] [Indexed: 09/16/2023] Open
Abstract
Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrences. Radiomics has the potential to address these obstacles by extracting quantitative, measurable, and extractable features from the region of interest in medical images. Medical imaging is a common source of information in clinical practice, presenting a potential alternative to biopsy, as it allows the extraction of a large number of features that, although not visible to the naked eye, may be relevant for tumour characterisation. Taking advantage of machine learning techniques, the set of features extracted when associated with biological parameters can be used for diagnosis, prognosis, and predictive accuracy valuable for clinical decision-making. Therefore, the main goal of this contribution was to determine to what extent the features extracted from Computed Tomography (CT) are related to cancer prognosis, namely Locoregional Recurrences (LRs), the development of Distant Metastases (DMs), and Overall Survival (OS). Through the set of tumour characteristics, predictive models were developed using machine learning techniques. The tumour was described by radiomic features, extracted from images, and by the clinical data of the patient. The performance of the models demonstrated that the most successful algorithm was XGBoost, and the inclusion of the patients' clinical data was an asset for cancer prognosis. Under these conditions, models were created that can reliably predict the LR, DM, and OS status, with the area under the ROC curve (AUC) values equal to 0.74, 0.84, and 0.91, respectively. In summary, the promising results obtained show the potential of radiomics, once the considered cancer prognosis can, in fact, be expressed through CT scans.
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Affiliation(s)
- Maria Gonçalves
- Center Algoritmi, LASI, University of Minho, 4710-057 Braga, Portugal
- Computer Algorithms for Medicine Laboratory, 8010 Graz, Austria
| | - Christina Gsaxner
- Computer Algorithms for Medicine Laboratory, 8010 Graz, Austria
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
| | - André Ferreira
- Center Algoritmi, LASI, University of Minho, 4710-057 Braga, Portugal
- Computer Algorithms for Medicine Laboratory, 8010 Graz, Austria
- Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Girardetstraße 2, 45131 Essen, Germany
| | - Jianning Li
- Computer Algorithms for Medicine Laboratory, 8010 Graz, Austria
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
- Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Girardetstraße 2, 45131 Essen, Germany
| | - Behrus Puladi
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Girardetstraße 2, 45131 Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR), Hufelandstraße 55, 45147 Essen, Germany
- German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, 45147 Essen, Germany
| | - Jan Egger
- Computer Algorithms for Medicine Laboratory, 8010 Graz, Austria
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
- Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Girardetstraße 2, 45131 Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR), Hufelandstraße 55, 45147 Essen, Germany
| | - Victor Alves
- Center Algoritmi, LASI, University of Minho, 4710-057 Braga, Portugal
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18
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Santer M, Kloppenburg M, Gottfried TM, Runge A, Schmutzhard J, Vorbach SM, Mangesius J, Riedl D, Mangesius S, Widmann G, Riechelmann H, Dejaco D, Freysinger W. Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma-A Systematic Review. Cancers (Basel) 2022; 14:5397. [PMID: 36358815 PMCID: PMC9654953 DOI: 10.3390/cancers14215397] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 10/28/2022] [Accepted: 10/29/2022] [Indexed: 07/22/2023] Open
Abstract
Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10-258) and of LNs was 340 (SD ± 268; range 21-791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43-99%) and for testing sets 86% (SD ± 5%; range 76-92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI's role in LN-classification in locally-advanced HNSCC.
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Affiliation(s)
- Matthias Santer
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Marcel Kloppenburg
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Timo Maria Gottfried
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Annette Runge
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Joachim Schmutzhard
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Samuel Moritz Vorbach
- Department of Radiation-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Julian Mangesius
- Department of Radiation-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - David Riedl
- University Hospital of Psychiatry II, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Ludwig-Boltzmann Institute for Rehabilitation Research, 1100 Vienna, Austria
| | - Stephanie Mangesius
- Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Gerlig Widmann
- Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Herbert Riechelmann
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Daniel Dejaco
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Wolfgang Freysinger
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
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Kim M, Lee JH, Joo L, Jeong B, Kim S, Ham S, Yun J, Kim N, Chung SR, Choi YJ, Baek JH, Lee JY, Kim JH. Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma. Korean J Radiol 2022; 23:1078-1088. [PMID: 36126954 PMCID: PMC9614290 DOI: 10.3348/kjr.2022.0299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/25/2022] [Accepted: 08/17/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. RESULTS Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set. CONCLUSION The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.
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Affiliation(s)
- Minjae Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.,Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jeong Hyun Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Leehi Joo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Boryeong Jeong
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Seonok Kim
- Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sungwon Ham
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jihye Yun
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - NamKug Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sae Rom Chung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Young Jun Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jung Hwan Baek
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ji Ye Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Ji-hoon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
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20
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Yang R, Hui D, Li X, Wang K, Li C, Li Z. Prediction of single pulmonary nodule growth by CT radiomics and clinical features - a one-year follow-up study. Front Oncol 2022; 12:1034817. [PMID: 36387220 PMCID: PMC9650464 DOI: 10.3389/fonc.2022.1034817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/05/2022] [Indexed: 09/07/2023] Open
Abstract
Background With the development of imaging technology, an increasing number of pulmonary nodules have been found. Some pulmonary nodules may gradually grow and develop into lung cancer, while others may remain stable for many years. Accurately predicting the growth of pulmonary nodules in advance is of great clinical significance for early treatment. The purpose of this study was to establish a predictive model using radiomics and to study its value in predicting the growth of pulmonary nodules. Materials and methods According to the inclusion and exclusion criteria, 228 pulmonary nodules in 228 subjects were included in the study. During the one-year follow-up, 69 nodules grew larger, and 159 nodules remained stable. All the nodules were randomly divided into the training group and validation group in a proportion of 7:3. For the training data set, the t test, Chi-square test and Fisher exact test were used to analyze the sex, age and nodule location of the growth group and stable group. Two radiologists independently delineated the ROIs of the nodules to extract the radiomics characteristics using Pyradiomics. After dimension reduction by the LASSO algorithm, logistic regression analysis was performed on age and ten selected radiological features, and a prediction model was established and tested in the validation group. SVM, RF, MLP and AdaBoost models were also established, and the prediction effect was evaluated by ROC analysis. Results There was a significant difference in age between the growth group and the stable group (P < 0.05), but there was no significant difference in sex or nodule location (P > 0.05). The interclass correlation coefficients between the two observers were > 0.75. After dimension reduction by the LASSO algorithm, ten radiomic features were selected, including two shape-based features, one gray-level-cooccurence-matrix (GLCM), one first-order feature, one gray-level-run-length-matrix (GLRLM), three gray-level-dependence-matrix (GLDM) and two gray-level-size-zone-matrix (GLSZM). The logistic regression model combining age and radiomics features achieved an AUC of 0.87 and an accuracy of 0.82 in the training group and an AUC of 0.82 and an accuracy of 0.84 in the verification group for the prediction of nodule growth. For nonlinear models, in the training group, the AUCs of the SVM, RF, MLP and boost models were 0.95, 1.0, 1.0 and 1.0, respectively. In the validation group, the AUCs of the SVM, RF, MLP and boost models were 0.81, 0.77, 0.81, and 0.71, respectively. Conclusions In this study, we established several machine learning models that can successfully predict the growth of pulmonary nodules within one year. The logistic regression model combining age and imaging parameters has the best accuracy and generalization. This model is very helpful for the early treatment of pulmonary nodules and has important clinical significance.
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Affiliation(s)
- Ran Yang
- Department of Radiology, Second People’s Hospital of JiuLongPo District, Chongqing, China
| | - Dongming Hui
- Department of Radiology, Second People’s Hospital of JiuLongPo District, Chongqing, China
| | - Xing Li
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Kun Wang
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Caiyong Li
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Zhichao Li
- Department of Radiology, Second People’s Hospital of JiuLongPo District, Chongqing, China
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Wang S, Jiang T, Hu X, Hu H, Zhou X, Wei Y, Mao X, Zhao Z. Can the combination of DWI and T2WI radiomics improve the diagnostic efficiency of cervical squamous cell carcinoma? Magn Reson Imaging 2022; 92:197-202. [PMID: 35842193 DOI: 10.1016/j.mri.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/27/2022] [Accepted: 07/11/2022] [Indexed: 12/09/2022]
Abstract
BACKGROUND To investigate the value of MRI multi-sequence imaging model in differentiation of cervical squamous cell carcinoma (CSCC). METHODS A total of 104 CSCC patients confirmed with pathology were retrospectively enrolled. All patients underwent conventional MRI examination before treatment. The lesions were segmented using ITK-SNAP software manually and radiomics features were extracted by Artificial Intelligence Kit (AK) software. 396 tumor texture features were obtained and then the mRMR and Lasso algorithms were used to reduce the feature dimension. Three models including T2WI model, DWI model and Joint model (combined TWI and DWI) were constructed in training group and evaluated in validation group. and the receiver operator characteristics and calibration curve were used to evaluate the model performance. RESULTS The Joint model and T2WI model both showed a better diagnostic efficacy than single DWI model in differentiation of CSCC in training group (Joint model: AUC = 0.841; T2WI model: AUC = 0.804; DWI model: AUC = 0.732) and validation group (Joint model: AUC = 0.822; T2WI model: AUC = 0.791; DWI model: AUC = 0.724). But there was no statistical difference between Joint model and T2WI model by Delong test(P > 0.05). CONCLUSIONS The study suggested that the conventional T2WI sequence may be more suitable for prognosis evaluation of CSCC, which can provide a potential tool to facilitate the differential diagnosis of low-differentiation and high-differentiation CSCC.
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Affiliation(s)
- Subo Wang
- Department of Radiology, Shaoxing Hospital of Trational medicine, Shaoxing 312000, Zhejiang Province, China.
| | - Tingchong Jiang
- Department of Radiology, Shaoxing Hospital of Trational medicine, Shaoxing 312000, Zhejiang Province, China
| | - Xi Hu
- Department of Radiology, Sir Run Run Shaw Hospital Affiliated to Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital Affiliated to Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Xiaoxuan Zhou
- Department of Radiology, Sir Run Run Shaw Hospital Affiliated to Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | | | - Xiaoming Mao
- Department of Radiology, Shaoxing Hospital of Trational medicine, Shaoxing 312000, Zhejiang Province, China.
| | - Zhenhua Zhao
- Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, China.
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Distinguishing Parotid Polymorphic Adenoma and Warthin Tumor Based on the CT Radiomics Nomogram: A Multicenter Study. Acad Radiol 2022; 30:717-726. [PMID: 35953356 DOI: 10.1016/j.acra.2022.06.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/20/2022]
Abstract
RATIONALE AND OBJECTIVES To develop, validate, and test a comprehensive radiomics prediction model to distinguish parotid polymorphic adenomas (PAs) and warthin tumors (WTs) using clinical data and enhanced computed tomography (CT) from a multicenter cohort. MATERIALS AND METHODS A total of 267 patients with PAs (n =172) or WTs (n = 95) from two hospitals were randomly divided into training (n =188) and validation (n =79) datasets. Radiomics features were extracted from the enhanced CT (arterial phase) followed by dimensionality reduction. Clinical and CT features were combined to establish a prediction model. A radiomics nomogram was constructed by combining RadScore and clinical factors. Moreover, an independent dataset of 31 patients from a third hospital was employed to test the model. Thus, the performance of the nomogram, radiomics signature, and clinical models was evaluated on the training, validation, and the independent testing datasets. Receiver operating characteristic (ROC) curves were used to compare the performance, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the model. RESULTS A total of 15 radiomics features were selected from CT data as the imaging markers to generate RadScores, and demographics or clinical data like age, sex, and smoking factors combined with RadScores were used to distinguish PAs and WTs based on multivariate logistic regression analyses. The results showed that radiomics nomograms combining clinical factors and RadScores provided satisfactory predictive values for distinguishing PAs from WTs, with areas under ROC curves (AUC) of 0.979, 0.922, and 0.903 for the training, validation, and the independent testing datasets, respectively. Decision curve analysis revealed that the radiomics nomogram outperformed the clinical factor models in terms of accuracy and effectiveness. CONCLUSION CT-based radiomics nomograms combining RadScores and clinical factors can be used to identify PAs and WTs, which may help tumor management by clinicians.
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Tardini E, Zhang X, Canahuate G, Wentzel A, Mohamed ASR, Van Dijk L, Fuller CD, Marai GE. Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad. J Med Internet Res 2022; 24:e29455. [PMID: 35442211 PMCID: PMC9069283 DOI: 10.2196/29455] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 09/03/2021] [Accepted: 02/09/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Currently, selection of patients for sequential versus concurrent chemotherapy and radiation regimens lacks evidentiary support and it is based on locally optimal decisions for each step. OBJECTIVE We aim to optimize the multistep treatment of patients with head and neck cancer and predict multiple patient survival and toxicity outcomes, and we develop, apply, and evaluate a first application of deep Q-learning (DQL) and simulation to this problem. METHODS The treatment decision DQL digital twin and the patient's digital twin were created, trained, and evaluated on a data set of 536 patients with oropharyngeal squamous cell carcinoma with the goal of, respectively, determining the optimal treatment decisions with respect to survival and toxicity metrics and predicting the outcomes of the optimal treatment on the patient. Of the data set of 536 patients, the models were trained on a subset of 402 (75%) patients (split randomly) and evaluated on a separate set of 134 (25%) patients. Training and evaluation of the digital twin dyad was completed in August 2020. The data set includes 3-step sequential treatment decisions and complete relevant history of the patient cohort treated at MD Anderson Cancer Center between 2005 and 2013, with radiomics analysis performed for the segmented primary tumor volumes. RESULTS On the test set, we found mean 87.35% (SD 11.15%) and median 90.85% (IQR 13.56%) accuracies in treatment outcome prediction, matching the clinicians' outcomes and improving the (predicted) survival rate by +3.73% (95% CI -0.75% to 8.96%) and the dysphagia rate by +0.75% (95% CI -4.48% to 6.72%) when following DQL treatment decisions. CONCLUSIONS Given the prediction accuracy and predicted improvement regarding the medically relevant outcomes yielded by this approach, this digital twin dyad of the patient-physician dynamic treatment problem has the potential of aiding physicians in determining the optimal course of treatment and in assessing its outcomes.
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Affiliation(s)
- Elisa Tardini
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Xinhua Zhang
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Guadalupe Canahuate
- Department of Electrical and Computer Engineering, University Of Iowa, Iowa City, IA, United States
| | - Andrew Wentzel
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Abdallah S R Mohamed
- MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, The University of Texas, Austin, TX, United States
| | | | - Clifton D Fuller
- MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, The University of Texas, Austin, TX, United States
| | - G Elisabeta Marai
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
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MR-Guided Adaptive Radiotherapy for OAR Sparing in Head and Neck Cancers. Cancers (Basel) 2022; 14:cancers14081909. [PMID: 35454816 PMCID: PMC9028510 DOI: 10.3390/cancers14081909] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Normal tissue toxicities in head and neck cancer persist as a cause of decreased quality of life and are associated with poorer treatment outcomes. The aim of this article is to review organ at risk (OAR) sparing approaches available in MR-guided adaptive radiotherapy and present future developments which hope to improve treatment outcomes. Increasing the spatial conformity of dose distributions in radiotherapy is an important first step in reducing normal tissue toxicities, and MR-guided treatment devices presents a new opportunity to use biological information to drive treatment decisions on a personalized basis. Abstract MR-linac devices offer the potential for advancements in radiotherapy (RT) treatment of head and neck cancer (HNC) by using daily MR imaging performed at the time and setup of treatment delivery. This article aims to present a review of current adaptive RT (ART) methods on MR-Linac devices directed towards the sparing of organs at risk (OAR) and a view of future adaptive techniques seeking to improve the therapeutic ratio. This ratio expresses the relationship between the probability of tumor control and the probability of normal tissue damage and is thus an important conceptual metric of success in the sparing of OARs. Increasing spatial conformity of dose distributions to target volume and OARs is an initial step in achieving therapeutic improvements, followed by the use of imaging and clinical biomarkers to inform the clinical decision-making process in an ART paradigm. Pre-clinical and clinical findings support the incorporation of biomarkers into ART protocols and investment into further research to explore imaging biomarkers by taking advantage of the daily MR imaging workflow. A coherent understanding of this road map for RT in HNC is critical for directing future research efforts related to sparing OARs using image-guided radiotherapy (IGRT).
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Intarak S, Chongpison Y, Vimolnoch M, Oonsiri S, Kitpanit S, Prayongrat A, Kannarunimit D, Chakkabat C, Sriswasdi S, Lertbutsayanukul C, Rakvongthai Y. Tumor Prognostic Prediction of Nasopharyngeal Carcinoma Using CT-Based Radiomics in Non-Chinese Patients. Front Oncol 2022; 12:775248. [PMID: 35155228 PMCID: PMC8831248 DOI: 10.3389/fonc.2022.775248] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/05/2022] [Indexed: 12/24/2022] Open
Abstract
PurposeWe aimed to construct predictive models for the overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) for nasopharyngeal carcinoma (NPC) patients by using CT-based radiomics.Materials and MethodsWe collected data from 197 NPC patients. For each patient, radiomic features were extracted from the CT image acquired at pretreatment via PyRadiomics. Feature selection was performed in two steps. First, features with high inter-observer variability based on multiple tumor delineations were excluded. Then, stratified bootstrappings were performed to identify feature combinations that most frequently achieved the highest (i) area under the receiver operating characteristic curve (AUC) for predicting 3-year OS, PFS, and DMFS or (ii) Harrell’s C-index for predicting time to event. Finally, regularized logistic regression and Cox proportional hazard models with the most frequently selected feature combinations as input were tuned using cross-validation. Additionally, we examined the robustness of the constructed model to variation in tumor delineation by simulating 100 realizations of radiomic feature values to mimic features extracted from different tumor boundaries.ResultsThe combined model that used both radiomics and clinical features yielded significantly higher AUC and Harrell’s C-index than models using either feature set alone for all outcomes (p < 0.05). The AUCs and Harrell’s C-indices of the clinical-only and radiomics-only models ranged from 0.758 ± 0.091 to 0.789 ± 0.082 and from 0.747 ± 0.062 to 0.767 ± 0.074, respectively. In comparison, the combined models achieved AUC of 0.801 ± 0.075 to 0.813 ± 0.078 and Harrell’s C-indices of 0.779 ± 0.066 to 0.796 ± 0.069. The results showed that our models were robust to variation in tumor delineation with the coefficient of variation ranging from 4.8% to 6.4% and from 6.7% to 9.3% for AUC and Harrell’s C-index, respectively.ConclusionOur results demonstrated that using CT-based radiomic features together with clinical features provided superior NPC prognostic prediction than using either clinical or radiomic features alone.
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Affiliation(s)
- Sararas Intarak
- Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yuda Chongpison
- Biostatistics Excellence Center, Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Mananchaya Vimolnoch
- Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Sornjarod Oonsiri
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Sarin Kitpanit
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Anussara Prayongrat
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Danita Kannarunimit
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chakkapong Chakkabat
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sira Sriswasdi
- Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center for Artificial Intelligence in Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chawalit Lertbutsayanukul
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yothin Rakvongthai
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- *Correspondence: Yothin Rakvongthai,
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Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.
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Volpe S, Pepa M, Zaffaroni M, Bellerba F, Santamaria R, Marvaso G, Isaksson LJ, Gandini S, Starzyńska A, Leonardi MC, Orecchia R, Alterio D, Jereczek-Fossa BA. Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist. Front Oncol 2021; 11:772663. [PMID: 34869010 PMCID: PMC8637856 DOI: 10.3389/fonc.2021.772663] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/25/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND AND PURPOSE Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT). MATERIALS AND METHODS Electronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1. RESULTS Forty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation). DISCUSSION AND CONCLUSION The range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.
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Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Federica Bellerba
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Riccardo Santamaria
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giulia Marvaso
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lars Johannes Isaksson
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Sara Gandini
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Anna Starzyńska
- Department of Oral Surgery, Medical University of Gdańsk, Gdańsk, Poland
| | - Maria Cristina Leonardi
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Roberto Orecchia
- Scientific Directorate, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Daniela Alterio
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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Bagher Ebadian H, Siddiqui F, Ghanem A, Zhu S, Lu M, Movsas B, Chetty IJ. Radiomics outperforms clinical factors in characterizing human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinomas. Biomed Phys Eng Express 2021; 8. [PMID: 34781281 DOI: 10.1088/2057-1976/ac39ab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 11/15/2021] [Indexed: 11/11/2022]
Abstract
Purpose:To utilize radiomic features extracted from CT images to characterize Human Papilloma Virus (HPV) for patients with oropharyngeal cancer squamous cell carcinoma (OPSCC).Methods:One hundred twenty-eight OPSCC patients with known HPV-status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomic features (11 feature-categories) were extracted in 3D from contrast-enhanced (CE)-CT images of gross-tumor-volumes using 'in-house' software ('ROdiomiX') developed and validated following the image-biomarker-standardization-initiative (IBSI) guidelines. Six clinical factors were investigated: Age-at-Diagnosis, Gender, Total-Charlson, Alcohol-Use, Smoking-History, and T-Stage. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) were applied to perform regularization in the radiomic and clinical feature spaces to identify the ranking of optimal feature subsets with most representative information for prediction of HPV. Lasso-GLM models/classifiers based on clinical factors only, radiomics only, and combined clinical and radiomics (ensemble/integrated) were constructed using random-permutation-sampling. Tests of significance (One-way ANOVA), average Area-Under-Receiver-Operating-Characteristic (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and prediction performance of the classifiers.Results:Five clinical factors, including T-stage, smoking status, and age, and 14 radiomic features, including tumor morphology, and intensity contrast were found to be statistically significant discriminators between HPV positive and negative cohorts. Performances for prediction of HPV for the 3 classifiers were: Radiomics-Lasso-GLM: AUC/PPV/NPV=0.789/0.755/0.805; Clinical-Lasso-GLM: 0.676/0.747/0.672, and Integrated/Ensemble-Lasso-GLM: 0.895/0.874/0.844. Results imply that the radiomics-based classifier enabled better characterization and performance prediction of HPV relative to clinical factors, and that the combination of both radiomics and clinical factors yields even higher accuracy characterization and predictive performance.Conclusion:Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of radiomic features towards characterization of HPV in patients with OPSCC.
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Affiliation(s)
- Hassan Bagher Ebadian
- Department of Radiation Oncology , Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Detroit, Michigan, 48202, UNITED STATES
| | - Farzan Siddiqui
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Ahmed Ghanem
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Simeng Zhu
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Mei Lu
- Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Benjamin Movsas
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, 48202, UNITED STATES
| | - Indrin J Chetty
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI 48202-2689, USA, Detroit, Michigan, 48202, UNITED STATES
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Devakumar D, Sunny G, Sasidharan BK, Bowen SR, Nadaraj A, Jeyseelan L, Mathew M, Irodi A, Isiah R, Pavamani S, John S, T Thomas HM. Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers. J Med Phys 2021; 46:181-188. [PMID: 34703102 PMCID: PMC8491314 DOI: 10.4103/jmp.jmp_6_21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 06/07/2021] [Accepted: 06/07/2021] [Indexed: 01/06/2023] Open
Abstract
Context: Cancer Radiomics is an emerging field in medical imaging and refers to the process of converting routine radiological images that are typically qualitatively interpreted to quantifiable descriptions of the tumor phenotypes and when combined with statistical analytics can improve the accuracy of clinical outcome prediction models. However, to understand the radiomic features and their correlation to molecular changes in the tumor, first, there is a need for the development of robust image analysis methods, software tools and statistical prediction models which is often limited in low- and middle-income countries (LMIC). Aims: The aim is to build a framework for machine learning of radiomic features of planning computed tomography (CT) and positron emission tomography (PET) using open source radiomics and data analytics platforms to make it widely accessible to clinical groups. The framework is tested in a small cohort to predict local disease failure following radiation treatment for head-and-neck cancer (HNC). The predictors were also compared with the existing Aerts HNC radiomics signature. Settings and Design: Retrospective analysis of patients with locally advanced HNC between 2017 and 2018 and 31 patients with both pre- and post-radiation CT and evaluation PET were selected. Subjects and Methods: Tumor volumes were delineated on baseline PET using the semi-automatic adaptive-threshold algorithm and propagated to CT; PyRadiomics features (total of 110 under shape/intensity/texture classes) were extracted. Two feature-selection methods were tested for model stability. Models were built based on least absolute shrinkage and selection operator-logistic and Ridge regression of the top pretreatment radiomic features and compared to Aerts' HNC-signature. Average model performance across all internal validation test folds was summarized by the area under the receiver operator curve (ROC). Results: Both feature selection methods selected CT features MCC (GLCM), SumEntropy (GLCM) and Sphericity (Shape) that could predict the binary failure status in the cross-validated group and achieved an AUC >0.7. However, models using Aerts' signature features (Energy, Compactness, GLRLM-GrayLevelNonUniformity and GrayLevelNonUniformity-HLH wavelet) could not achieve a clear separation between outcomes (AUC = 0.51–0.54). Conclusions: Radiomics pipeline included open-source workflows which makes it adoptable in LMIC countries. Additional independent validation of data is crucial for the implementation of radiomic models for clinical risk stratification.
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Affiliation(s)
- Devadhas Devakumar
- Department of Nuclear Medicine, Christian Medical College, Vellore, Tamil Nadu, India
| | - Goutham Sunny
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India.,Department of Radiation Oncology, Baptist Cancer Centre, Bangalore Baptist Hospital, Bengaluru, Karnataka, India
| | | | - Stephen R Bowen
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Ambily Nadaraj
- Department of Clinical Epidemiology, Christian Medical College, Vellore, Tamil Nadu, India
| | - L Jeyseelan
- Department of Clinical Epidemiology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Manu Mathew
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Aparna Irodi
- Department of Radiology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Rajesh Isiah
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Simon Pavamani
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Subhashini John
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Hannah Mary T Thomas
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
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Can we use radiomics in ultrasound imaging? Impact of preprocessing on feature repeatability. Diagn Interv Imaging 2021; 102:659-667. [PMID: 34690106 DOI: 10.1016/j.diii.2021.10.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/07/2021] [Accepted: 10/07/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE The purpose of this study was to assess the inter-slice radiomic feature repeatability in ultrasound imaging and the impact of preprocessing using intensity standardization and grey-level discretization to help improve radiomics reproducibility. MATERIALS AND METHODS This single-center study enrolled consecutive patients with an orbital lesion who underwent ultrasound examination of the orbit from December 2015 to July 2019. Two images per lesion were randomly assigned to two subsets. Radiomic features were extracted and inter-slice repeatability was assessed using the intraclass correlation coefficient (ICC) between the subsets. The impact of preprocessing on feature repeatability was assessed using image intensity standardization with or without outliers removal on whole images, bounding boxes or regions of interest (ROI), and fixed bin size or fixed bin number grey-level discretization. Number of inter-slice repeatable features (ICC ≥0.7) between methods was compared. RESULTS Eighty-eight patients (37 men, 51 women) with a mean age of 51.5 ± 17 (SD) years (range: 20-88 years) were enrolled. Without preprocessing, 29/101 features (28.7%) were repeatable between slices. The greatest number of repeatable features (41/101) was obtained using intensity standardization with outliers removal on the ROI and fixed bin size discretization. Standardization performed better with outliers removal than without (P < 0.001), and on ROIs than on native images (P < 0.001). Fixed bin size discretization performed better than fixed bin number (P = 0.008). CONCLUSION Radiomic features extracted from ultrasound images are impacted by the slice and preprocessing. The use of intensity standardization with outliers removal applied to the ROI and a fixed bin size grey-level discretization may improve feature repeatability.
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Zheng YM, Chen J, Xu Q, Zhao WH, Wang XF, Yuan MG, Liu ZJ, Wu ZJ, Dong C. Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland. Dentomaxillofac Radiol 2021; 50:20210023. [PMID: 33950705 PMCID: PMC8474129 DOI: 10.1259/dmfr.20210023] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE: Preoperative differentiation between parotid Warthin's tumor (WT) and pleomorphic adenoma (PMA) is crucial for treatment decisions. The purpose of this study was to establish and validate an MRI-based radiomics nomogram for preoperative differentiation between WT and PMA. METHODS AND MATERIALS A total of 127 patients with histological diagnosis of WT or PMA from two clinical centres were enrolled in training set (n = 75; WT = 34, PMA = 41) and external test set (n = 52; WT = 24, PMA = 28). Radiomics features were extracted from axial T1WI and fs-T2WI images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. A clinical factors model was built using demographics and MRI findings. A radiomics nomogram combining the independent clinical factors and Rad-score was constructed. The receiver operating characteristic analysis was used to assess the performance levels of the nomogram, radiomics signature and clinical model. RESULTS The radiomics nomogram incorporating the age and radiomics signature showed favourable predictive value for differentiating parotid WT from PMA, with AUCs of 0.953 and 0.918 for the training set and test set, respectively. CONCLUSIONS The MRI-based radiomics nomogram had good performance in distinguishing parotid WT from PMA, which could optimize clinical decision-making.
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Affiliation(s)
- Ying-mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jiao Chen
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Qi Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wen-hui Zhao
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xin-feng Wang
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ming-gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao Universtity, Qingdao, China
| | - Zong-jing Liu
- Department of Pediatric Hematology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zeng-jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Song B, Yang K, Garneau J, Lu C, Li L, Lee J, Stock S, Braman NM, Koyuncu CF, Toro P, Fu P, Koyfman SA, Lewis JS, Madabhushi A. Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis. Front Oncol 2021; 11:744250. [PMID: 34557418 PMCID: PMC8454409 DOI: 10.3389/fonc.2021.744250] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/16/2021] [Indexed: 12/18/2022] Open
Abstract
Purpose There is a lack of biomarkers for accurately prognosticating outcome in both human papillomavirus-related (HPV+) and tobacco- and alcohol-related (HPV-) oropharyngeal squamous cell carcinoma (OPSCC). The aims of this study were to i) develop and evaluate radiomic features within (intratumoral) and around tumor (peritumoral) on CT scans to predict HPV status; ii) investigate the prognostic value of the radiomic features for both HPV- and HPV+ patients, including within individual AJCC eighth edition-defined stage groups; and iii) develop and evaluate a clinicopathologic imaging nomogram involving radiomic, clinical, and pathologic factors for disease-free survival (DFS) prediction for HPV+ patients. Experimental Design This retrospective study included 582 OPSCC patients, of which 462 were obtained from The Cancer Imaging Archive (TCIA) with available tumor segmentation and 120 were from Cleveland Clinic Foundation (CCF, denoted as SCCF) with HPV+ OPSCC. We subdivided the TCIA cohort into training (ST, 180 patients) and validation (SV, 282 patients) based on an approximately 3:5 ratio for HPV status prediction. The top 15 radiomic features that were associated with HPV status were selected by the minimum redundancy-maximum relevance (MRMR) using ST and evaluated on SV. Using 3 of these 15 top HPV status-associated features, we created radiomic risk scores for both HPV+ (RRSHPV+) and HPV- patients (RRSHPV-) through a Cox regression model to predict DFS. RRSHPV+ was further externally validated on SCCF. Nomograms for the HPV+ population (Mp+RRS) were constructed. Both RRSHPV+ and Mp+RRS were used to prognosticate DFS for the AJCC eighth edition-defined stage I, stage II, and stage III patients separately. Results RRSHPV+ was prognostic for DFS for i) the whole HPV+ population [hazard ratio (HR) = 1.97, 95% confidence interval (CI): 1.35-2.88, p < 0.001], ii) the AJCC eighth stage I population (HR = 1.99, 95% CI: 1.04-3.83, p = 0.039), and iii) the AJCC eighth stage II population (HR = 3.61, 95% CI: 1.71-7.62, p < 0.001). HPV+ nomogram Mp+RRS (C-index, 0.59; 95% CI: 0.54-0.65) was also prognostic of DFS (HR = 1.86, 95% CI: 1.27-2.71, p = 0.001). Conclusion CT-based radiomic signatures are associated with both HPV status and DFS in OPSCC patients. With additional validation, the radiomic signature and its corresponding nomogram could potentially be used for identifying HPV+ OPSCC patients who might be candidates for therapy deintensification.
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Affiliation(s)
- Bolin Song
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Kailin Yang
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, United States
| | - Jonathan Garneau
- Department of Otolaryngology and Head and Neck Surgery, University of Virginia, Charlottesville, VA, United States
| | - Cheng Lu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Lin Li
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Jonathan Lee
- Imaging Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Sarah Stock
- Imaging Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Nathaniel M Braman
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Can Fahrettin Koyuncu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Paula Toro
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Shlomo A Koyfman
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, United States
| | - James S Lewis
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Anant Madabhushi
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, United States.,Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States
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Rodrigues A, Loman K, Nawrocki J, Hoang JK, Chang Z, Mowery YM, Oyekunle T, Niedzwiecki D, Brizel DM, Craciunescu O. Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma. Front Oncol 2021; 11:708398. [PMID: 34540674 PMCID: PMC8444263 DOI: 10.3389/fonc.2021.708398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
The purpose of this study was to assess baseline variability in histogram and texture features derived from apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (DW-MRI) examinations and to identify early treatment-induced changes to these features in patients with head and neck squamous cell carcinoma (HNSCC) undergoing definitive chemoradiation. Patients with American Joint Committee on Cancer Stage III–IV (7th edition) HNSCC were prospectively enrolled on an IRB-approved study to undergo two pre-treatment baseline DW-MRI examinations, performed 1 week apart, and a third early intra-treatment DW-MRI examination during the second week of chemoradiation. Forty texture and six histogram features were derived from ADC maps. Repeatability of the features from the baseline ADC maps was assessed with the intra-class correlation coefficient (ICC). A Wilcoxon signed-rank test compared average baseline and early treatment feature changes. Data from nine patients were used for this study. Comparison of the two baseline ADC maps yielded 11 features with an ICC ≥ 0.80, indicating that these features had excellent repeatability: Run Gray-Level Non-Uniformity, Coarseness, Long Zone High Gray-Level, Variance (Histogram Feature), Cluster Shade, Long Zone, Variance (Texture Feature), Run Length Non-Uniformity, Correlation, Cluster Tendency, and ADC Median. The Wilcoxon signed-rank test resulted in four features with significantly different early treatment-induced changes compared to the baseline values: Run Gray-Level Non-Uniformity (p = 0.005), Run Length Non-Uniformity (p = 0.005), Coarseness (p = 0.006), and Variance (Histogram) (p = 0.006). The feasibility of histogram and texture analysis as a potential biomarker is dependent on the baseline variability of each metric, which disqualifies many features.
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Affiliation(s)
- Anna Rodrigues
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Kelly Loman
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Jeff Nawrocki
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Jenny K Hoang
- Department of Radiology, Johns Hopkins University, Baltimore, MD, United States
| | - Zheng Chang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Yvonne M Mowery
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Taofik Oyekunle
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Donna Niedzwiecki
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - David M Brizel
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.,Department of Head and Neck Surgery and Communication Sciences, Duke University Medical Center, Durham, NC, United States
| | - Oana Craciunescu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
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Patel H, Vock DM, Marai GE, Fuller CD, Mohamed ASR, Canahuate G. Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features. Sci Rep 2021; 11:14057. [PMID: 34234160 PMCID: PMC8263609 DOI: 10.1038/s41598-021-92072-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/10/2021] [Indexed: 11/27/2022] Open
Abstract
To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster label was included together with other clinical data to train an ensemble model using five predictive models (Cox, random forest, RSF, logistic regression, and logistic-elastic net). Ensemble performance was evaluated over the independent test set for both recurrence free survival (RFS) and overall survival (OS). The Kaplan-Meier curves for OS stratified by cluster label show significant differences for both training and testing (p val < 0.0001). When compared to the models trained using clinical data only, the inclusion of the cluster label improves AUC test performance from .62 to .79 and from .66 to .80 for OS and RFS, respectively. The extraction of a single feature, namely a cluster label, to represent the high-dimensional radiomic feature space reduces the dimensionality and sparsity of the data. Moreover, inclusion of the cluster label improves model performance compared to clinical data only and offers comparable performance to the models including raw radiomic features.
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Affiliation(s)
- Harsh Patel
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, 52242, USA
| | - David M Vock
- Division of Biostatistics, University of Minnesota, Minneapolis, 55455, USA
| | - G Elisabeta Marai
- Department of Department of Computer Science, University of Illinois at Chicago, Chicago, 60607, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, 77030, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, 77030, USA
| | - Guadalupe Canahuate
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, 52242, USA.
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Gul M, Bonjoc KJC, Gorlin D, Wong CW, Salem A, La V, Filippov A, Chaudhry A, Imam MH, Chaudhry AA. Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers. Front Oncol 2021; 11:639326. [PMID: 34307123 PMCID: PMC8293690 DOI: 10.3389/fonc.2021.639326] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 06/08/2021] [Indexed: 11/21/2022] Open
Abstract
Radiomics is an emerging field in radiology that utilizes advanced statistical data characterizing algorithms to evaluate medical imaging and objectively quantify characteristics of a given disease. Due to morphologic heterogeneity and genetic variation intrinsic to neoplasms, radiomics have the potential to provide a unique insight into the underlying tumor and tumor microenvironment. Radiomics has been gaining popularity due to potential applications in disease quantification, predictive modeling, treatment planning, and response assessment - paving way for the advancement of personalized medicine. However, producing a reliable radiomic model requires careful evaluation and construction to be translated into clinical practices that have varying software and/or medical equipment. We aim to review the diagnostic utility of radiomics in otorhinolaryngology, including both cancers of the head and neck as well as the thyroid.
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Affiliation(s)
- Maryam Gul
- Amaze Research Foundation, Department of Biomarker Discovery, Anaheim, CA, United States
| | - Kimberley-Jane C. Bonjoc
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - David Gorlin
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Chi Wah Wong
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Amirah Salem
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Vincent La
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Aleksandr Filippov
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Abbas Chaudhry
- Amaze Research Foundation, Department of Biomarker Discovery, Anaheim, CA, United States
| | - Muhammad H. Imam
- Florida Cancer Specialists, Department of Oncology, Orlando, FL, United States
| | - Ammar A. Chaudhry
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
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FH T, CYW C, EYW C. Radiomics AI prediction for head and neck squamous cell carcinoma (HNSCC) prognosis and recurrence with target volume approach. BJR Open 2021; 3:20200073. [PMID: 34381946 PMCID: PMC8320130 DOI: 10.1259/bjro.20200073] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/15/2021] [Accepted: 04/29/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES To evaluate the performance of radiomics features extracted from planning target volume (PTV) and gross tumor volume (GTV) in the prediction of the death prognosis and cancer recurrence rate for head and neck squamous cell carcinoma (HNSCC). METHODS 188 HNSCC patients' planning CT images with radiotherapy structures sets were acquired from Cancer Imaging Archive (TCIA). The 3D slicer (v. 4.10.2) with the PyRadiomics extension (Computational Imaging and Bioinformatics Lab, Harvard medical School) was used to extract radiomics features from the radiotherapy planning images. An in-house developed deep learning artificial neural networks (DL-ANN) model was used to predict death prognosis and cancer recurrence rate based on the features extracted from GTV and PTV of the CT images. RESULTS The PTV radiomics features with DL-ANN model could achieve 77.7% accuracy with overall AUC equal to 0.934 and 0.932 when predicting HNSCC-related death prognosis and cancer recurrence respectively. Furthermore, the DL-ANN model can achieve an accuracy of 74.3% with AUC equal to 0.947 and 0.956 for the HNSCC-related death prognosis and cancer recurrence respectively using GTV features. CONCLUSION Using both GTV and PTV radiomics features in the DL-ANN model, can aid in predicting HNSCC-related death prognosis and cancer recurrence. Clinicians may find it helpful in formulating different treatment regimens and facilitate personized medicine based on the predicted outcome when performing GTV and PTV delineation. ADVANCES IN KNOWLEDGE Radiomics features of GTV and PTV are reliable prognosis and recurrence predicting tools, which may help clinicians in GTV and PTV delineation to facilitate delivery of personalized treatment.
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Affiliation(s)
- Tang FH
- School of Medical and Health Sciences, Tung Wah College, Hong Kong, Hong Kong
| | - Chu CYW
- School of Medical and Health Sciences, Tung Wah College, Hong Kong, Hong Kong
| | - Cheung EYW
- School of Medical and Health Sciences, Tung Wah College, Hong Kong, Hong Kong
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Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv 2021; 49:107739. [PMID: 33794304 DOI: 10.1016/j.biotechadv.2021.107739] [Citation(s) in RCA: 265] [Impact Index Per Article: 88.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/01/2021] [Accepted: 03/25/2021] [Indexed: 02/06/2023]
Abstract
With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.
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Affiliation(s)
- Parminder S Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Smarti Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Ewan Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Emily Jefferson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
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Fatima K, Dasgupta A, DiCenzo D, Kolios C, Quiaoit K, Saifuddin M, Sandhu M, Bhardwaj D, Karam I, Poon I, Husain Z, Sannachi L, Czarnota GJ. Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma. Clin Transl Radiat Oncol 2021; 28:62-70. [PMID: 33778174 PMCID: PMC7985224 DOI: 10.1016/j.ctro.2021.03.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/23/2021] [Accepted: 03/07/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE This study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC). METHODS Fifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation. RESULTS The median follow up for the entire group was 38 months (range 7-64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment. CONCLUSION QUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC.Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.
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Key Words
- AAC, Average acoustic concentration
- ACE, Attenuation co-efficient estimate
- ASD, Average scatterer diameter
- AUC, Area under the curve
- Acc, Accuracy
- CON, Contrast
- COR, Correlation
- CR, Complete responders
- CT, Computed tomography
- Delta-radiomics
- EBV, Epstein-Barr virus
- ENE, Energy
- FDG-PET, 18F-fluorodeoxyglucose positron emission tomography
- FLD, Fisher’s linear discriminant
- FN, False negative
- FP, False positive
- GLCM, Grey level co-occurrence matrix
- HN, Head and neck
- HNSCC, Head and neck squamous cell carcinoma
- HOM, Homogeneity
- HPV, Human papillomavirus
- Head and neck malignancy
- IGRT, Image-guided radiation therapy
- IMRT, Intensity-modulated radiation therapy
- MBF, Mid-band fit
- MRI, Magnetic resonance imaging
- Machine learning
- NR, Non-recurrence
- PET, Positron emission tomography
- PR, Partial responders
- QUS, Quantitative ultrasound
- Quantitative ultrasound
- R, Recurrence
- RF, Radiofrequency
- RFS, Recurrence-free survival
- ROI, Region of interest
- RT, Radiotherapy
- Radiomics
- Radiotherapy squamous cell carcinoma
- Recurrence
- SAS, Spacing among scatterers
- SI, Spectral intercept
- SP, Specificity
- SS, Spectral slope
- SVM, Support vector machine
- Sn, Sensitivity
- TN, True negative
- TP, True positive
- US, Ultrasound
- kNN, k nearest neighbors
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Affiliation(s)
- Kashuf Fatima
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Archya Dasgupta
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Daniel DiCenzo
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Karina Quiaoit
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Michael Sandhu
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Divya Bhardwaj
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Irene Karam
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Ian Poon
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zain Husain
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | | | - Gregory J. Czarnota
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
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Zheng YM, Xu WJ, Hao DP, Liu XJ, Gao CP, Tang GZ, Li J, Wang HX, Dong C. A CT-based radiomics nomogram for differentiation of lympho-associated benign and malignant lesions of the parotid gland. Eur Radiol 2021; 31:2886-2895. [PMID: 33123791 DOI: 10.1007/s00330-020-07421-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 09/25/2020] [Accepted: 10/13/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Preoperative differentiation between benign lymphoepithelial lesion (BLEL) and mucosa-associated lymphoid tissue lymphoma (MALToma) in the parotid gland is important for treatment decisions. The purpose of this study was to develop and validate a CT-based radiomics nomogram combining radiomics signature and clinical factors for the preoperative differentiation of BLEL from MALToma in the parotid gland. METHODS A total of 101 patients with BLEL (n = 46) or MALToma (n = 55) were divided into a training set (n = 70) and validation set (n = 31). Radiomics features were extracted from non-contrast CT images, a radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factor model. A radiomics nomogram combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The performance levels of the nomogram, radiomics signature, and clinical model were evaluated and validated on the training and validation datasets, and then compared among the three models. RESULTS Seven features were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature showed favorable predictive value for differentiating parotid BLEL from MALToma, with AUCs of 0.983 and 0.950 for the training set and validation set, respectively. Decision curve analysis showed that the nomogram outperformed the clinical factor model in terms of clinical usefulness. CONCLUSIONS The CT-based radiomics nomogram incorporating the Rad-score and clinical factors showed favorable predictive efficacy for differentiating BLEL from MALToma in the parotid gland, and may help in the clinical decision-making process. KEY POINTS • Differential diagnosis between BLEL and MALToma in parotid gland is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of BLEL from MALToma with improved diagnostic efficacy.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Wen-Jian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Da-Peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Xue-Jun Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Chuan-Ping Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Guo-Zhang Tang
- Department of Cardiac Ultrasound, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - He-Xiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China.
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Wang X, Dai S, Wang Q, Chai X, Xian J. Investigation of MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas. Jpn J Radiol 2021; 39:755-762. [PMID: 33860416 DOI: 10.1007/s11604-021-01116-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 04/01/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To develop and validate an MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas (SCCs). MATERIALS AND METHODS One-hundred-and-fifty-four patients were enrolled (74 individuals with SCCs and 80 with lymphomas). After feature analysis and feature selection with variance threshold and least absolute shrinkage and selection operator (LASSO) methods, an MRI-based radiomics model with the support vector machine (SVM) classifier was constructed in differentiation between lymphomas and SCCs. Areas under the receiver operating characteristic curves (AUCs) of the MRI-based radiomics model were compared with those of radiologists using Delong test. RESULTS Five features (T1 original shape Compactness2, T1 wavelet-HHH first-order Total Energy, T2 wavelet-HLH GLCM Informational Measure of Correlation1, T1 wavelet-LHL GLCM Inverse Variance and T1 square GLRLM Long Run Low Gray Level Emphasis) were finally selected in the radiomics model. The AUC values in differentiation between lymphomas and SCCs were 0.94 for the training dataset and 0.85 for the validation dataset, respectively. For all the patient datasets, the AUC values of radiomics model, readers 1, 2 and 3 were 0.92, 0.76, 0.77 and 0.80, respectively. For the validation datasets, no significant difference was found between the AUCs of the radiomics model and those of the three radiologist (P = 0.459, 0.469, 0.738 for radiologist 1, 2 and 3, respectively). CONCLUSION An MRI-based radiomics model can help to differentiate sinonasal lymphomas from SCCs with high accuracy.
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Affiliation(s)
- Xinyan Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | | | - Qian Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Xiangfei Chai
- Huiying Medical Technology Co., Ltd., Beijing, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
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Barua S, Elhalawani H, Volpe S, Al Feghali KA, Yang P, Ng SP, Elgohari B, Granberry RC, Mackin DS, Gunn GB, Hutcheson KA, Chambers MS, Court LE, Mohamed ASR, Fuller CD, Lai SY, Rao A. Computed Tomography Radiomics Kinetics as Early Imaging Correlates of Osteoradionecrosis in Oropharyngeal Cancer Patients. Front Artif Intell 2021; 4:618469. [PMID: 33898983 PMCID: PMC8063205 DOI: 10.3389/frai.2021.618469] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 03/04/2021] [Indexed: 01/08/2023] Open
Abstract
Osteoradionecrosis (ORN) is a major side-effect of radiation therapy in oropharyngeal cancer (OPC) patients. In this study, we demonstrate that early prediction of ORN is possible by analyzing the temporal evolution of mandibular subvolumes receiving radiation. For our analysis, we use computed tomography (CT) scans from 21 OPC patients treated with Intensity Modulated Radiation Therapy (IMRT) with subsequent radiographically-proven ≥ grade II ORN, at three different time points: pre-IMRT, 2-months, and 6-months post-IMRT. For each patient, radiomic features were extracted from a mandibular subvolume that developed ORN and a control subvolume that received the same dose but did not develop ORN. We used a Multivariate Functional Principal Component Analysis (MFPCA) approach to characterize the temporal trajectories of these features. The proposed MFPCA model performs the best at classifying ORN vs. Control subvolumes with an area under curve (AUC) = 0.74 [95% confidence interval (C.I.): 0.61–0.90], significantly outperforming existing approaches such as a pre-IMRT features model or a delta model based on changes at intermediate time points, i.e., at 2- and 6-month follow-up. This suggests that temporal trajectories of radiomics features derived from sequential pre- and post-RT CT scans can provide markers that are correlates of RT-induced mandibular injury, and consequently aid in earlier management of ORN.
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Affiliation(s)
- Souptik Barua
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Stefania Volpe
- Department of Radiation Oncology, European Institute of Oncology IRCSS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Karine A Al Feghali
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Pei Yang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sweet Ping Ng
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Robin C Granberry
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Dennis S Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - G Brandon Gunn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Katherine A Hutcheson
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mark S Chambers
- Department of Oncologic Dentistry and Prosthodontics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Stephen Y Lai
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Arvind Rao
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
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Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics. Sci Rep 2021; 11:6117. [PMID: 33731738 PMCID: PMC7969626 DOI: 10.1038/s41598-021-85221-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 02/23/2021] [Indexed: 12/24/2022] Open
Abstract
To investigate the role of quantitative ultrasound (QUS) radiomics to predict treatment response in patients with head and neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). Five spectral parameters, 20 texture, and 80 texture-derivative features were extracted from the index lymph node before treatment. Response was assessed initially at 3 months with complete responders labelled as early responders (ER). Patients with residual disease were followed to classify them as either late responders (LR) or patients with persistent/progressive disease (PD). Machine learning classifiers with leave-one-out cross-validation was used for the development of a binary response-prediction radiomics model. A total of 59 patients were included in the study (22 ER, 29 LR, and 8 PD). A support vector machine (SVM) classifier led to the best performance with accuracy and area under curve (AUC) of 92% and 0.91, responsively to define the response at 3 months (ER vs. LR/PD). The 2-year recurrence-free survival for predicted-ER, LR, PD using an SVM-model was 91%, 78%, and 27%, respectively (p < 0.01). Pretreatment QUS-radiomics using texture derivatives in HNSCC can predict the response to RT with an accuracy of more than 90% with a strong influence on the survival. Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.
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Liu Y, Zheng J, Zhao J, Yu L, Lu X, Zhu Z, Guo C, Zhang T. Magnetic resonance image biomarkers improve differentiation of benign and malignant parotid tumors through diagnostic model analysis. Oral Radiol 2021; 37:658-668. [PMID: 33428106 DOI: 10.1007/s11282-020-00504-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 12/21/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVES To explore the effectiveness of magnetic resonance image (MRI)-based biomarkers for identifying benign and malignant parotid tumors via diagnostic model analysis. METHODS This retrospective study included 109 patients (development cohort and validation cohort) who underwent MRI preoperatively, including T1- and T2-weighted images. Parameters based on 2D or 3D texture analysis were extracted from tumor lesions by MaZda software, fisher discriminant and bootstrap method were used to perform parameter reduction, diagnostic models with the selected biomarkers were established along with clinical data, model performance (discrimination and calibration) was furtherly evaluated by internal and external validation, decision curve analysis was applied to measure the improvement of clinical benefits. RESULTS S(5,5) Entrop, S(0,1) ASM, WavEnHH (s-4), S(1,1,0) Entropy and Perc.10% were significantly associated with the pathological diagnosis of parotid tumor (benign versus malignancy), when adding these biomarkers to the regression analysis, model performance significantly improved in the development cohort (likelihood-ratio-test; p < 0.05, with an increase of AUC from 0.72 (reference model) to 0.85), and these results were maintained in a small external validation cohort. Decision curve analysis indicated that clinical benefit was greater with the application of MRI-based biomarkers. CONCLUSIONS MRI-based texture analysis is proven to be an effective tool in differentiating benign and malignant parotid tumors, preoperative diagnosis was improved with the selected biomarkers compared to the reference model.
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Affiliation(s)
- Yuebo Liu
- Department of Stomatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiabao Zheng
- Department of Implant Dentistry, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Jizhi Zhao
- Department of Stomatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lijiang Yu
- Department of Stomatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoping Lu
- Department of Radiology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Peking Union Medical College, Beijing, China
| | - Zhihui Zhu
- Department of Stomatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chunlan Guo
- Department of Stomatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Zhang
- Department of Stomatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Dasgupta A, Fatima K, DiCenzo D, Bhardwaj D, Quiaoit K, Saifuddin M, Karam I, Poon I, Husain Z, Tran WT, Sannachi L, Czarnota GJ. Quantitative ultrasound radiomics in predicting recurrence for patients with node-positive head-neck squamous cell carcinoma treated with radical radiotherapy. Cancer Med 2020; 10:2579-2589. [PMID: 33314716 PMCID: PMC8026932 DOI: 10.1002/cam4.3634] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 11/05/2020] [Accepted: 11/05/2020] [Indexed: 12/24/2022] Open
Abstract
This prospective study was conducted to investigate the role of quantitative ultrasound (QUS) radiomics in predicting recurrence for patients with node‐positive head‐neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). The most prominent cervical lymph node (LN) was scanned with a clinical ultrasound device having central frequency of 6.5 MHz. Ultrasound radiofrequency data were processed to obtain 7 QUS parameters. Color‐coded parametric maps were generated based on individual QUS spectral features corresponding to each of the smaller units. A total of 31 (7 primary QUS and 24 texture) features were obtained before treatment. All patients were treated with radical RT and followed according to standard institutional practice. Recurrence (local, regional, or distant) served as an endpoint. Three different machine learning classifiers with a set of maximally three features were used for model development and tested with leave‐one‐out cross‐validation for nonrecurrence and recurrence groups. Fifty‐one patients were included, with a median follow up of 38 months (range 7–64 months). Recurrence was observed in 17 patients. The best results were obtained using a k‐nearest neighbor (KNN) classifier with a sensitivity, specificity, accuracy, and an area under curve of 76%, 71%, 75%, and 0.74, respectively. All the three features selected for the KNN model were texture features. The KNN‐model‐predicted 3‐year recurrence‐free survival was 81% and 40% in the predicted no‐recurrence and predicted‐recurrence groups, respectively. (p = 0.001). The pilot study demonstrates pretreatment QUS‐radiomics can predict the recurrence group with an accuracy of 75% in patients with node‐positive HNSCC. Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.
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Affiliation(s)
- Archya Dasgupta
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Kashuf Fatima
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Daniel DiCenzo
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Divya Bhardwaj
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Karina Quiaoit
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Irene Karam
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Ian Poon
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zain Husain
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - William T Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Gregory J Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
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Bagher-Ebadian H, Chetty IJ. Technical Note: ROdiomiX: A validated software for radiomics analysis of medical images in radiation oncology. Med Phys 2020; 48:354-365. [PMID: 33169367 DOI: 10.1002/mp.14590] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 10/27/2020] [Accepted: 11/03/2020] [Indexed: 12/19/2022] Open
Abstract
PURPOSE This study introduces an in-house-designed software platform (ROdiomiX) for the radiomics analysis of medical images in radiation oncology. ROdiomiX is a MATLAB-based framework for the computation of radiomic features and feature aggregation techniques in compliance with the Image-Biomarker-Standardization-Initiative (IBSI) guidelines, which includes preprocessing protocols and quantitative benchmark results for analysis of computational phantom images. METHODS AND MATERIALS The ROdiomiX software system consists of a series of computation cores implemented on the basis of the guidelines proposed by the IBSI. It is capable of quantitative computation of the following 11 different feature categories: Local-Intensity, Intensity-Histogram, Intensity-Based-Statistical, Intensity-Volume-Histogram, Gray-Level-Co-occurrence, Gray-Level-Run-Length, Gray-Level-Size-Zone, Gray-Level-Distance-Zone, Neighborhood-Grey-Tone-Difference, Neighboring-Grey-Level-Dependence, and Morphological feature. ROdiomiX was validated against benchmark values for the IBSI 3D digital phantom, as well as one designed in-house (HFH). The intraclass correlation coefficient (ICC) for estimating the degree of absolute agreement between ROdiomiX computation and benchmark values for different features at the 95% confidence level (CL) was used for comparison. RESULTS Among the 11 feature categories with 149 total features including 10 different feature aggregation methods (following the IBSI guidelines), the percent difference between absolute feature values computed by the ROdiomiX software and benchmark values reported for IBSI and HFH digital phantoms were 0.14% + 0.43% and 0.11% + 0.27%, respectively. The ICC values were >0.997 for all ten feature categories for both the IBSI and HFH digital phantoms. CONCLUSION The authors successfully developed a platform for computation of quantitative radiomic features. The image preprocessing and computational software cores were designed following the procedures specified by the IBSI. Benchmarking testing was in excellent agreement against the IBSI- and HFH-designed computational phantoms.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA
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Zhang L, Ye Z, Ruan L, Jiang M. Pretreatment MRI-Derived Radiomics May Evaluate the Response of Different Induction Chemotherapy Regimens in Locally advanced Nasopharyngeal Carcinoma. Acad Radiol 2020; 27:1655-1664. [PMID: 33004261 DOI: 10.1016/j.acra.2020.09.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/01/2020] [Accepted: 09/03/2020] [Indexed: 01/04/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate and compare the performance of radiomics in predicting induction chemotherapy response treated with two different regimens in patients with advanced nasopharyngeal carcinoma. MATERIALS AND METHODS A total of 265 patients with pathologically confirmed locally advanced nasopharyngeal carcinoma (stage II-IV), including 115 treated with gemcitabine plus cisplatin (GP group) and 150 treated with docetaxel plus cisplatin (TP group) were retrospectively enrolled. Radiomics features were extracted from the volume of interest delineated in multi-MR sequences on a 3T scanner. After random stratified grouping (training and validation cohorts) and logistic regression based on selected features, the association between the radiomics signature and the early response to induction chemotherapy were established for GP and TP regiments, respectively. RESULTS Clinical factors showed no significant difference between the response and non-response groups for the GP and TP regiments (all p > 0.05). The accuracy of the radiomics signature consisting of selected features from the joint T1, T2, and T1C in the GP group (0.852 in the training cohort vs. 0.853 in the validation cohort) was significantly higher than that in the TP group (0.774 vs 0.727). The overall performance of the GP model was steady, with efficiency to distinguish responders from nonresponders with an AUC reaching 0.907 (95% confidence interval [CI] [0.843-0.970]) in the training cohort and 0.886 (95% CI [0.772-0.998]) in the validation cohort, while leveling at 0.800 (95% CI [0.712-0.888]) in the training cohort and 0.863 (95% CI [0.758-0.967]) in the validation cohort in the TP group. CONCLUSION Pretreatment MR radiomics signature can better predict the early response to IC in the GP regimen than the TP regimen, which may be helpful to guide IC management.
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Affiliation(s)
- Lei Zhang
- Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, China; Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China; Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, China
| | - Zhimin Ye
- Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, China; Department of Radiotherapy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China; Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, China
| | - Lei Ruan
- Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, China; Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China; Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, China
| | - Mingxiang Jiang
- Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, China; Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China; Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, China.
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Delago D, Knittelfelder O, Jakse G, Lukasiak K, Reinisch S, Renner W, Stranzl-Lawatsch H, Partl R, Langsenlehner T. The decreased mean platelet volume is associated with poor prognosis in patients with oropharyngeal cancer treated with radiotherapy. Radiat Oncol 2020; 15:259. [PMID: 33160368 PMCID: PMC7648964 DOI: 10.1186/s13014-020-01702-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 10/29/2020] [Indexed: 01/23/2023] Open
Abstract
Background There is considerable evidence that platelets contribute to cancer growth and metastatic dissemination. In recent studies, altered mean platelet volume (MPV) has been associated with prognosis in different types of cancer. However, the prognostic role of the MPV in head and neck squamous cell cancer (HNSCC) is currently discussed controversially. The present study was performed to analyze and further elucidate the prognostic significance of the MPV in HNSCC. Methods A total of 319 oropharyngeal squamous cell cancer (OPSCC) patients treated with radiotherapy at a tertiary academic center were enrolled in the present study. Kaplan–Meier method as well as uni- and multivariate Cox proportional hazards were used to evaluate the impact of MPV on cancer-specific survival (CSS), locoregional control (LC) and recurrence-free survival (RFS). Results The median MPV was 10.30 fL (mean 10.26 ± 1.17fL). Univariate analyses showed a significant association of the MPV with CSS (HR 0.85, 95% CI 0.74–0.98, p = 0.025), LC (HR 0.86, 95% CI 0.74–0.99, p = 0.034) and RFS (HR 0.87, 95% CI 0.76–0.996; p = 0.043). In multivariate analysis, the MPV remained an independent prognostic factor for CSS (HR 0.77, 95% CI 0.63–0.93, p = 0.008), LC (HR 0.80, 95% CI 0.65–0.98, p = 0.030), and RFS (HR 0.83, 95% CI 0.685–0.999, p = 0.049). Conclusions Our findings indicate that the MPV is a prognostic marker in OPSCC patients and may contribute to future individual risk assessment.
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Affiliation(s)
- Daniela Delago
- Department of Therapeutic Radiology and Oncology, Comprehensive Cancer Center, Medical University of Graz, Auenbruggerplatz 32, 8036, Graz, Austria
| | - Olivia Knittelfelder
- Department of Therapeutic Radiology and Oncology, Comprehensive Cancer Center, Medical University of Graz, Auenbruggerplatz 32, 8036, Graz, Austria
| | - Gabriele Jakse
- Department of Therapeutic Radiology and Oncology, Comprehensive Cancer Center, Medical University of Graz, Auenbruggerplatz 32, 8036, Graz, Austria
| | - Katarzyna Lukasiak
- Department of Therapeutic Radiology and Oncology, Comprehensive Cancer Center, Medical University of Graz, Auenbruggerplatz 32, 8036, Graz, Austria
| | - Sabine Reinisch
- Department of Otorhinolaryngology, Comprehensive Cancer Center, Medical University of Graz, Auenbruggerplatz 26, 8036, Graz, Austria
| | - Wilfried Renner
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Auenbruggerplatz 15, 8036, Graz, Austria
| | - Heidi Stranzl-Lawatsch
- Department of Therapeutic Radiology and Oncology, Comprehensive Cancer Center, Medical University of Graz, Auenbruggerplatz 32, 8036, Graz, Austria
| | - Richard Partl
- Department of Therapeutic Radiology and Oncology, Comprehensive Cancer Center, Medical University of Graz, Auenbruggerplatz 32, 8036, Graz, Austria.
| | - Tanja Langsenlehner
- Department of Therapeutic Radiology and Oncology, Comprehensive Cancer Center, Medical University of Graz, Auenbruggerplatz 32, 8036, Graz, Austria
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Defeudis A, De Mattia C, Rizzetto F, Calderoni F, Mazzetti S, Torresin A, Vanzulli A, Regge D, Giannini V. Standardization of CT radiomics features for multi-center analysis: impact of software settings and parameters. Phys Med Biol 2020; 65:195012. [PMID: 32575082 DOI: 10.1088/1361-6560/ab9f61] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The aim of this multicentric study is an inter-center benchmarking, to assess how different set tools applied to the same radiomics workflow affected the radiomics features (RFs) values. This topic is of key importance to start collaboration between different centers and to bring radiomic studies from benchmark to bedside. A per-lesion analysis was performed on 56 metastases (mts) selected from 14 patients. A single radiologist performed the segmentation of all mts, and RFs were extracted from the same segmentation of each mts, using two different software and file formats. Potential sources of discrepancies were evaluated. The intraclass correlation coefficient was used to describe how strongly the same radiomic measurements calculated in the two different centers resemble each other. Moreover, means of the relative changes of each RF were calculated, compared and gradually reduced. We showed that, after matching all formulas, discrepancies in RFs calculation between two centers ranged from 1% to 277%. Therefore, we evaluated other sources of variability using a stepwise approach, which led us to reduce the inter-center discrepancies to 0% for 22/25 RFs and below 2% for 3 RFs out of 25. In this study we demonstrated that different radiomic applications and masks formats might strongly impact the computation of some RFs. Therefore, when dealing with multi-center studies it is mandatory to adopt all strategies that can help in limiting the differences, thus keeping in mind the feasibility of these strategies in large cohort studies.
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Affiliation(s)
- Arianna Defeudis
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy. Department of Surgical Sciences, University of Turin, Turin, Italy
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Martens RM, Koopman T, Noij DP, Pfaehler E, Übelhör C, Sharma S, Vergeer MR, Leemans CR, Hoekstra OS, Yaqub M, Zwezerijnen GJ, Heymans MW, Peeters CFW, de Bree R, de Graaf P, Castelijns JA, Boellaard R. Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma. EJNMMI Res 2020; 10:102. [PMID: 32894373 PMCID: PMC7477048 DOI: 10.1186/s13550-020-00686-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 08/13/2020] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (18F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy. METHODS Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent 18F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order 18F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with 18F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients' outcome. RESULTS Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764). CONCLUSIONS Combining HPV-status, first-order 18F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care. TRIAL REGISTRATION Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://www.trialregister.nl/trial/3946.
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Affiliation(s)
- Roland M Martens
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands.
| | - Thomas Koopman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Daniel P Noij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Elisabeth Pfaehler
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Caroline Übelhör
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Sughandi Sharma
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Marije R Vergeer
- Department of Radiation Oncology, Amsterdam University Medical Center, De Boelelaan, 1117, Amsterdam, Netherlands
| | - C René Leemans
- Department of Otolaryngology-Head and Neck Surgery, Amsterdam University Medical Center, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Otto S Hoekstra
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Gerben J Zwezerijnen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Martijn W Heymans
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Carel F W Peeters
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Jonas A Castelijns
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands.,Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies. Future Sci OA 2020; 6:FSO624. [PMID: 33235811 PMCID: PMC7668124 DOI: 10.2144/fsoa-2020-0073] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Aim: We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT). Materials & methods: QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders. Results: Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively. Conclusion: QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment. Patients with head and neck cancer are often treated with radiation, which usually spans over 6–7 weeks. The response is usually measured 3 months after treatment completion. In this study, we had performed ultrasound scans from the patient’s neck node during radiation treatment (after 24 h, 1 and 4 weeks). Artificial intelligence was used to interpret the ultrasound imaging and predict the response to radiation at the end of 3 months. The scans obtained after the first week were able to predict the treatment response with reasonable accuracy (86%).
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