1
|
Petrou E, Chatzipapas K, Papadimitroulas P, Andrade-Miranda G, Katsakiori PF, Papathanasiou ND, Visvikis D, Kagadis GC. Investigation of Machine and Deep Learning Techniques to Detect HPV Status. J Pers Med 2024; 14:737. [PMID: 39063991 PMCID: PMC11278505 DOI: 10.3390/jpm14070737] [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: 06/14/2024] [Revised: 07/03/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND This study investigated alternative, non-invasive methods for human papillomavirus (HPV) detection in head and neck cancers (HNCs). We compared two approaches: analyzing computed tomography (CT) scans with a Deep Learning (DL) model and using radiomic features extracted from CT images with machine learning (ML) models. METHODS Fifty patients with histologically confirmed HNC were included. We first trained a modified ResNet-18 DL model on CT data to predict HPV status. Next, radiomic features were extracted from manually segmented regions of interest near the oropharynx and used to train four ML models (K-Nearest Neighbors, logistic regression, decision tree, random forest) for the same purpose. RESULTS The CT-based model achieved the highest accuracy (90%) in classifying HPV status. Among the ML models, K-Nearest Neighbors performed best (80% accuracy). Weighted Ensemble methods combining the CT-based model with each ML model resulted in moderate accuracy improvements (70-90%). CONCLUSIONS Our findings suggest that CT scans analyzed by DL models hold promise for non-invasive HPV detection in HNC. Radiomic features, while less accurate in this study, offer a complementary approach. Future research should explore larger datasets and investigate the potential of combining DL and radiomic techniques.
Collapse
Affiliation(s)
- Efstathia Petrou
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece (G.C.K.)
| | - Konstantinos Chatzipapas
- Laboratoire de Traitement de l’Information Médicale (LaTIM), UMR 1101, INSERM, University of Brest, 29200 Brest, France
| | - Panagiotis Papadimitroulas
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece (G.C.K.)
| | - Gustavo Andrade-Miranda
- Laboratoire de Traitement de l’Information Médicale (LaTIM), UMR 1101, INSERM, University of Brest, 29200 Brest, France
| | - Paraskevi F. Katsakiori
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece (G.C.K.)
| | - Nikolaos D. Papathanasiou
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece (G.C.K.)
| | - Dimitris Visvikis
- Laboratoire de Traitement de l’Information Médicale (LaTIM), UMR 1101, INSERM, University of Brest, 29200 Brest, France
| | - George C. Kagadis
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece (G.C.K.)
| |
Collapse
|
2
|
Sim Y, Kim M, Kim J, Lee SK, Han K, Sohn B. Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques. Eur Radiol 2024; 34:3102-3112. [PMID: 37848774 DOI: 10.1007/s00330-023-10338-3] [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: 08/29/2022] [Revised: 07/08/2023] [Accepted: 08/20/2023] [Indexed: 10/19/2023]
Abstract
OBJECTIVES To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC). METHODS This retrospective, multicenter study included consecutive patients with newly diagnosed and pathologically confirmed OPSCC between January 2017 and December 2020 (110 patients in the training set, 44 patients in the external validation set). A total of 293 radiomics features were extracted from three sequences (T2-weighted images [T2WI], contrast-enhanced T1-weighted images [CE-T1WI], and ADC). Combinations of three feature selection, five oversampling, and 12 machine learning techniques were evaluated to optimize its diagnostic performance. The area under the receiver operating characteristic curve (AUC) of the top five models was validated in the external validation set. RESULTS A total of 154 patients (59.2 ± 9.1 years; 132 men [85.7%]) were included, and oversampling was employed to account for data imbalance between HPV-positive and HPV-negative OPSCC (86.4% [133/154] vs. 13.6% [21/154]). For the ADC radiomics model, the combination of random oversampling and ridge showed the highest diagnostic performance in the external validation set (AUC, 0.791; 95% CI, 0.775-0.808). The ADC radiomics model showed a higher trend in diagnostic performance compared to the radiomics model using CE-T1WI (AUC, 0.604; 95% CI, 0.590-0.618), T2WI (AUC, 0.695; 95% CI, 0.673-0.717), and a combination of both (AUC, 0.642; 95% CI, 0.626-0.657). CONCLUSIONS The ADC radiomics model using random oversampling and ridge showed the highest diagnostic performance in predicting the HPV status of OPSCC in the external validation set. CLINICAL RELEVANCE STATEMENT Among multiple sequences, the ADC radiomics model has a potential for generalizability and applicability in clinical practice. Exploring multiple oversampling and machine learning techniques was a valuable strategy for optimizing radiomics model performance. KEY POINTS • Previous radiomics studies using multiparametric MRI were conducted at single centers without external validation and had unresolved data imbalances. • Among the ADC, CE-T1WI, and T2WI radiomics models and the ADC histogram models, the ADC radiomics model was the best-performing model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma. • The ADC radiomics model with the combination of random oversampling and ridge showed the highest diagnostic performance.
Collapse
Affiliation(s)
- Yongsik Sim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Minjae Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Jinna Kim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung-Koo Lee
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Beomseok Sohn
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
| |
Collapse
|
3
|
Ansari G, Mirza-Aghazadeh-Attari M, Mosier KM, Fakhry C, Yousem DM. Radiomics Features in Predicting Human Papillomavirus Status in Oropharyngeal Squamous Cell Carcinoma: A Systematic Review, Quality Appraisal, and Meta-Analysis. Diagnostics (Basel) 2024; 14:737. [PMID: 38611650 PMCID: PMC11011663 DOI: 10.3390/diagnostics14070737] [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: 12/20/2023] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
We sought to determine the diagnostic accuracy of radiomics features in predicting HPV status in oropharyngeal squamous cell carcinoma (SCC) compared to routine paraclinical measures used in clinical practice. Twenty-six articles were included in the systematic review, and thirteen were used for the meta-analysis. The overall sensitivity of the included studies was 0.78, the overall specificity was 0.76, and the overall area under the ROC curve was 0.84. The diagnostic odds ratio (DOR) equaled 12 (8, 17). Subgroup analysis showed no significant difference between radiomics features extracted from CT or MR images. Overall, the studies were of low quality in regard to radiomics quality score, although most had a low risk of bias based on the QUADAS-2 tool. Radiomics features showed good overall sensitivity and specificity in determining HPV status in OPSCC, though the low quality of the included studies poses problems for generalizability.
Collapse
Affiliation(s)
- Golnoosh Ansari
- Department of Radiology, Northwestern Hospital, Northwestern School of Medicine, Chicago, IL 60611, USA;
| | - Mohammad Mirza-Aghazadeh-Attari
- Division of Interventional Radiology, Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Kristine M. Mosier
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Carole Fakhry
- Department of Otolaryngology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA;
| | - David M. Yousem
- Division of Neuroradiology, Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA;
| |
Collapse
|
4
|
Kawashima Y, Fujita A, Buch K, Qureshi MM, Li B, Takumi K, Rai A, Chapman MN, Sakai O. Using Texture Analysis of Neck Computed Tomography Images to Differentiate Primary Hyperparathyroidism From Normal Controls. J Comput Assist Tomogr 2024; 48:137-142. [PMID: 37531643 DOI: 10.1097/rct.0000000000001517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
OBJECTIVE To investigate the utility of texture analysis in detecting osseous changes associated with hyperparathyroidism on neck CT examinations compared with control patients and to explore the best regions in the head and neck to evaluate changes in the trabecular architecture secondary to hyperparathyroidism. METHODS Patients with hyperparathyroidism who underwent a 4D CT of the neck with contrast were included in this study. Age-matched control patients with no history of hyperparathyroidism who underwent a contrast-enhanced neck CT were also included. Mandibular condyles, bilateral mandibular bodies, the body of the C4 vertebra, the manubrium of the sternum, and bilateral clavicular heads were selected for analysis, and oval-shaped regions of interest were manually placed. These segmented areas were imported into an in-house developed texture analysis program, and 41 texture analysis features were extracted. A mixed linear regression model was used to compare differences in the texture analysis features contoured at each of the osseous structures between patients with hyperparathyroidism and age-matched control patients. RESULTS A total of 30 patients with hyperparathyroidism and 30 age-matched control patients were included in this study. Statistically significant differences in texture features between patients with hyperparathyroidism and control patients in all 8 investigated osseous regions. The sternum showed the greatest number of texture features with statistically significant differences between these groups. CONCLUSIONS Some CT texture features demonstrated statistically significant differences between patients with hyperparathyroidism and control patients. The results suggest that texture features may discriminate changes in the osseous architecture of the head and neck in patients with hyperparathyroidism.
Collapse
Affiliation(s)
| | | | | | - M Mustafa Qureshi
- Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA
| | - Baojun Li
- From the Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA
| | | | - Aayushi Rai
- From the Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA
| | | | | |
Collapse
|
5
|
Jo KH, Kim J, Cho H, Kang WJ, Lee SK, Sohn B. 18F-FDG PET/CT Parameters Enhance MRI Radiomics for Predicting Human Papilloma Virus Status in Oropharyngeal Squamous Cell Carcinoma. Yonsei Med J 2023; 64:738-744. [PMID: 37992746 PMCID: PMC10681825 DOI: 10.3349/ymj.2023.0187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/26/2023] [Accepted: 08/17/2023] [Indexed: 11/24/2023] Open
Abstract
PURPOSE Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients with OPSCC. MATERIALS AND METHODS Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set. RESULTS A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564-0.959], 0.638 (95% CI, 0.404-0.871), and 0.823 (95% CI, 0.668-0.978) for Models 1, 2, and 3, respectively. The net reclassification improvement of Model 3, compared with that of Model 1, in the test set was 0.119. CONCLUSION When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC.
Collapse
Affiliation(s)
- Kwan Hyeong Jo
- Department of Nuclear Medicine, Korea University Guro Hospital, Seoul, Korea
| | - Jinna Kim
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hojin Cho
- Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Won Jun Kang
- Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Beomseok Sohn
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
| |
Collapse
|
6
|
Bicci E, Calamandrei L, Mungai F, Granata V, Fusco R, De Muzio F, Bonasera L, Miele V. Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date. Infect Agent Cancer 2023; 18:58. [PMID: 37814320 PMCID: PMC10563217 DOI: 10.1186/s13027-023-00530-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/11/2023] [Indexed: 10/11/2023] Open
Abstract
The tumours of head and neck district are around 3% of all malignancies and squamous cell carcinoma is the most frequent histotype, with rapid increase during the last two decades because of the increment of the infection due to human papilloma virus (HPV). Even if the gold standard for the diagnosis is histological examination, including the detection of viral DNA and transcription products, imaging plays a fundamental role in the detection and staging of HPV + tumours, in order to assess the primary tumour, to establish the extent of disease and for follow-up. The main diagnostic tools are Computed Tomography (CT), Positron Emission Tomography-Computed Tomography (PET-CT) and Magnetic Resonance Imaging (MRI), but also Ultrasound (US) and the use of innovative techniques such as Radiomics have an important role. Aim of our review is to illustrate the main imaging features of HPV + tumours of the oropharynx, in US, CT and MRI imaging. In particular, we will outline the main limitations and strengths of the various imaging techniques, the main uses in the diagnosis, staging and follow-up of disease and the fundamental differential diagnoses of this type of tumour. Finally, we will focus on the innovative technique of texture analysis, which is increasingly gaining importance as a diagnostic tool in aid of the radiologist.
Collapse
Affiliation(s)
- Eleonora Bicci
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy.
| | - Leonardo Calamandrei
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Francesco Mungai
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Naples, 80013, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, 20122, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, Campobasso, 86100, Italy
| | - Luigi Bonasera
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Vittorio Miele
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| |
Collapse
|
7
|
Altinok O, Guvenis A. Interpretable radiomics method for predicting human papillomavirus status in oropharyngeal cancer using Bayesian networks. Phys Med 2023; 114:102671. [PMID: 37708571 DOI: 10.1016/j.ejmp.2023.102671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/14/2023] [Accepted: 09/06/2023] [Indexed: 09/16/2023] Open
Abstract
OBJECTIVES To develop a simple interpretable Bayesian Network (BN) to classify HPV status in patients with oropharyngeal cancer. METHODS Two hundred forty-six patients, 216 of whom were HPV positive, were used in this study. We extracted 851 radiomics markers from patients' contrast-enhanced Computed Tomography (CT) images. Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. The area under the curve (AUC) demonstrated BN model performance in 30% of the data reserved for testing. A Support Vector Machine (SVM) based method was also implemented for comparison purposes. RESULTS The Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. Areas under the Curves (AUC) were found 0.78 and 0.72 on the training and test data, respectively. When using support vector machine (SVM) and 25 features, the AUC was found 0.83 on the test data. CONCLUSIONS The straightforward structure and power of interpretability of our BN model will help clinicians make treatment decisions and enable the non-invasive detection of HPV status from contrast-enhanced CT images. Higher accuracy can be obtained using more complex structures at the expense of lower interpretability. ADVANCES IN KNOWLEDGE Radiomics is being studied lately as a simple imaging data based HPV status detection technique which can be an alternative to laboratory approaches. However, it generally lacks interpretability. This work demonstrated the feasibility of using Bayesian networks based radiomics for predicting HPV positivity in an interpretable way.
Collapse
Affiliation(s)
- Oya Altinok
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey; Biomedical Engineering, Namik Kemal University, Tekirdağ, Turkey.
| | - Albert Guvenis
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| |
Collapse
|
8
|
Glogauer J, Kohanzadeh A, Feit A, Fournier JE, Zians A, Somogyi DZ. The Use of Radiomic Features to Predict Human Papillomavirus (HPV) Status in Head and Neck Tumors: A Review. Cureus 2023; 15:e44476. [PMID: 37664330 PMCID: PMC10472720 DOI: 10.7759/cureus.44476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2023] [Indexed: 09/05/2023] Open
Abstract
Head and neck cancers represent a significant source of morbidity and mortality across the world. The individual genetic makeup of each tumor can help to determine the course of treatment and can help clinicians predict prognosis. Non-invasive tools to determine the genetic status of these tumors, particularly p16 (human papillomavirus (HPV)) status could prove extremely valuable to treating clinicians and surgeons. The field of radiomics is a burgeoning area of radiology practice that aims to provide quantitative biomarkers that can be derived from radiological images and could prove useful in determining p16 status non-invasively. In this review, we summarize the current evidence for the use of radiomics to determine the HPV status of head and neck tumors. .
Collapse
Affiliation(s)
- Judah Glogauer
- Department of Pathology and Molecular Medicine, McMaster University, Waterloo, CAN
| | | | - Avery Feit
- Medical School, Albert Einstein College of Medicine, Bronx, USA
| | - Jeffrey E Fournier
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, CAN
| | - Avraham Zians
- Department of Diagnostic and Interventional Radiology, Montefiore Medical Center, Wakefield Campus, Bronx, USA
| | - Dafna Z Somogyi
- Department of Internal Medicine, Westchester Medical Center, Valhalla, USA
| |
Collapse
|
9
|
Salahuddin Z, Chen Y, Zhong X, Woodruff HC, Rad NM, Mali SA, Lambin P. From Head and Neck Tumour and Lymph Node Segmentation to Survival Prediction on PET/CT: An End-to-End Framework Featuring Uncertainty, Fairness, and Multi-Region Multi-Modal Radiomics. Cancers (Basel) 2023; 15:1932. [PMID: 37046593 PMCID: PMC10093277 DOI: 10.3390/cancers15071932] [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: 02/21/2023] [Revised: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
Automatic delineation and detection of the primary tumour (GTVp) and lymph nodes (GTVn) using PET and CT in head and neck cancer and recurrence-free survival prediction can be useful for diagnosis and patient risk stratification. We used data from nine different centres, with 524 and 359 cases used for training and testing, respectively. We utilised posterior sampling of the weight space in the proposed segmentation model to estimate the uncertainty for false positive reduction. We explored the prognostic potential of radiomics features extracted from the predicted GTVp and GTVn in PET and CT for recurrence-free survival prediction and used SHAP analysis for explainability. We evaluated the bias of models with respect to age, gender, chemotherapy, HPV status, and lesion size. We achieved an aggregate Dice score of 0.774 and 0.760 on the test set for GTVp and GTVn, respectively. We observed a per image false positive reduction of 19.5% and 7.14% using the uncertainty threshold for GTVp and GTVn, respectively. Radiomics features extracted from GTVn in PET and from both GTVp and GTVn in CT are the most prognostic, and our model achieves a C-index of 0.672 on the test set. Our framework incorporates uncertainty estimation, fairness, and explainability, demonstrating the potential for accurate detection and risk stratification.
Collapse
Affiliation(s)
- Zohaib Salahuddin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Yi Chen
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6200 MD Maastricht, The Netherlands
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Xian Zhong
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology and Reproduction, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands
| | - Nastaran Mohammadian Rad
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Shruti Atul Mali
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology and Reproduction, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands
| |
Collapse
|
10
|
Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review. Cancers (Basel) 2023; 15:cancers15041174. [PMID: 36831517 PMCID: PMC9954362 DOI: 10.3390/cancers15041174] [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: 12/21/2022] [Revised: 01/31/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.
Collapse
|
11
|
Ito K, Kurasawa M, Sugimori T, Muraoka H, Hirahara N, Sawada E, Negishi S, Kasai K, Kaneda T. Risk assessment of external apical root resorption associated with orthodontic treatment using computed tomography texture analysis. Oral Radiol 2023; 39:75-82. [PMID: 35303210 DOI: 10.1007/s11282-022-00604-3] [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: 12/27/2021] [Accepted: 02/26/2022] [Indexed: 01/07/2023]
Abstract
OBJECTIVES This study aimed to quantitatively assess maxillary central incisor roots using pre-orthodontics computed tomography (CT) texture analysis as part of a radiomics quantitative analysis. METHODS This retrospective case-control study included 16 patients with external apical root resorption (EARR) and 16 age- and sex-matched patients without EARR, after orthodontic treatment who underwent pre-orthodontics CT for jaw deformities. All patients were treated with a fixed orthodontic appliance before and after surgical orthodontic treatment. EARR was defined as root resorption ≥ 2 mm of the left and right maxillary central incisors on CT images more than 2 years after the start of orthodontic treatment. Texture features of the maxillary central incisor with and without EARR after orthodontic treatment were analyzed using the open-access software, MaZda Ver. 3.3. Ten texture features were selected using the Fisher method in MaZda from 279 original parameters, which were calculated for each of the maxillary central incisors with and without EARR. The results were tested using the Student's t test, Welch's t test, or Mann-Whitney U test. RESULTS Four gray-level run length matrix features and six gray-level co-occurrence matrix features displayed significant differences between both the groups (p < 0.01). CONCLUSIONS CT texture analysis was able to quantitatively assess maxillary central incisor roots and distinguish between maxillary central incisor roots with and without EARR. CT texture analysis may be a useful method for predicting EARR after orthodontic treatment.
Collapse
Affiliation(s)
- Kotaro Ito
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan.
| | - Mayu Kurasawa
- Department of Orthodontics, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Tadasu Sugimori
- Department of Orthodontics, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Hirotaka Muraoka
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Naohisa Hirahara
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Eri Sawada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Shinichi Negishi
- Department of Orthodontics, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Kazutaka Kasai
- Department of Orthodontics, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Takashi Kaneda
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| |
Collapse
|
12
|
Sminia P, Guipaud O, Viktorsson K, Ahire V, Baatout S, Boterberg T, Cizkova J, Dostál M, Fernandez-Palomo C, Filipova A, François A, Geiger M, Hunter A, Jassim H, Edin NFJ, Jordan K, Koniarová I, Selvaraj VK, Meade AD, Milliat F, Montoro A, Politis C, Savu D, Sémont A, Tichy A, Válek V, Vogin G. Clinical Radiobiology for Radiation Oncology. RADIOBIOLOGY TEXTBOOK 2023:237-309. [DOI: 10.1007/978-3-031-18810-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
AbstractThis chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.
Collapse
|
13
|
HPV impact on oropharyngeal cancer radiological staging: 7th vs 8th edition of AJCC TNM classification. Clin Imaging 2022; 93:39-45. [DOI: 10.1016/j.clinimag.2022.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/04/2022] [Accepted: 10/18/2022] [Indexed: 11/05/2022]
|
14
|
Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature. Cancers (Basel) 2022; 14:cancers14102445. [PMID: 35626048 PMCID: PMC9139172 DOI: 10.3390/cancers14102445] [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: 03/29/2022] [Revised: 05/02/2022] [Accepted: 05/10/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary The incidence of squamous cell carcinomas of the oropharynx has rapidly increased in the last two decades due to human papilloma virus infection (HPV). HPV-positive and HPV-negative squamous cell tumours differ in radiological imaging, treatment, and prognosis; therefore, differential diagnosis is mandatory. Radiomics with texture analysis is an innovative technique that has been used increasingly in recent years to characterise the tissue heterogeneity of certain structures such as neoplasms or organs by measuring the spatial distribution of pixel values on radiological imaging. This review delineates the application of texture analysis in oropharyngeal tumours and explores how radiomics may potentially improve clinical decision-making. Abstract Human papilloma virus infection (HPV) is associated with the development of lingual and palatine tonsil carcinomas. Diagnosing, differentiating HPV-positive from HPV-negative cancers, and assessing the presence of lymph node metastases or recurrences by the visual interpretation of images is not easy. Texture analysis can provide structural information not perceptible to human eyes. A systematic literature search was performed on 16 February 2022 for studies with a focus on texture analysis in oropharyngeal cancers. We conducted the research on PubMed, Scopus, and Web of Science platforms. Studies were screened for inclusion according to the preferred reporting items for systematic reviews. Twenty-six studies were included in our review. Nineteen articles related specifically to the oropharynx and seven articles analysed the head and neck area with sections dedicated to the oropharynx. Six, thirteen, and seven articles used MRI, CT, and PET, respectively, as the imaging techniques by which texture analysis was performed. Regarding oropharyngeal tumours, this review delineates the applications of texture analysis in (1) the diagnosis, prognosis, and assessment of disease recurrence or persistence after therapy, (2) early differentiation of HPV-positive versus HPV-negative cancers, (3) the detection of cancers not visualised by imaging alone, and (4) the assessment of lymph node metastases from unknown primary carcinomas.
Collapse
|
15
|
The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment. Neuroradiology 2022; 64:1639-1647. [PMID: 35459957 PMCID: PMC9271107 DOI: 10.1007/s00234-022-02959-0] [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: 12/08/2021] [Accepted: 04/07/2022] [Indexed: 11/19/2022]
Abstract
Purpose
Human papillomavirus (HPV) status assessment is crucial for decision making in oropharyngeal cancer patients. In last years, several articles have been published investigating the possible role of radiomics in distinguishing HPV-positive from HPV-negative neoplasms. Aim of this review was to perform a systematic quality assessment of radiomic studies published on this topic. Methods Radiomics studies on HPV status prediction in oropharyngeal cancer patients were selected. The Radiomic Quality Score (RQS) was assessed by three readers to evaluate their methodological quality. In addition, possible correlations between RQS% and journal type, year of publication, impact factor, and journal rank were investigated. Results After the literature search, 19 articles were selected whose RQS median was 33% (range 0–42%). Overall, 16/19 studies included a well-documented imaging protocol, 13/19 demonstrated phenotypic differences, and all were compared with the current gold standard. No study included a public protocol, phantom study, or imaging at multiple time points. More than half (13/19) included feature selection and only 2 were comprehensive of non-radiomic features. Mean RQS was significantly higher in clinical journals. Conclusion Radiomics has been proposed for oropharyngeal cancer HPV status assessment, with promising results. However, these are supported by low methodological quality investigations. Further studies with higher methodological quality, appropriate standardization, and greater attention to validation are necessary prior to clinical adoption. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-022-02959-0.
Collapse
|
16
|
Siravegna G, O'Boyle CJ, Varmeh S, Queenan N, Michel A, Stein J, Thierauf J, Sadow PM, Faquin WC, Perry SK, Bard AZ, Wang W, Deschler DG, Emerick KS, Varvares MA, Park JC, Clark JR, Chan AW, Andreu Arasa VC, Sakai O, Lennerz J, Corcoran RB, Wirth LJ, Lin DT, Iafrate AJ, Richmon JD, Faden DL. Cell free HPV DNA provides an accurate and rapid diagnosis of HPV-associated head and neck cancer. Clin Cancer Res 2021; 28:719-727. [PMID: 34857594 DOI: 10.1158/1078-0432.ccr-21-3151] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/15/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE HPV-associated Head and Neck Squamous Cell Carcinoma(HPV+HNSCC) is the most common HPV-associated malignancy in the United States and continues to increase in incidence. Current diagnostic approaches for HPV+HNSCC rely on tissue biopsy followed by histomorphologic assessment and detection of HPV indirectly by p16 immunohistochemistry. Such approaches are invasive and have variable sensitivity. EXPERIMENTAL DESIGN We conducted a prospective observational study in 140 subjects (70 cases and 70 controls) to test the hypothesis that a non-invasive diagnostic approach for HPV+HNSCC would have improved diagnostic accuracy, lower cost, and shorter Diagnostic Interval compared to standard approaches. Blood was collected, processed for circulating tumor HPV DNA(ctHPVDNA) and analyzed with custom ddPCR assays for HPV genotypes 16,18, 33, 35 and 45. Diagnostic performance, cost and Diagnostic Interval were calculated for standard clinical work up and compared to a non-invasive approach using ctHPVDNA combined with cross-sectional imaging and physical exam findings. RESULTS Sensitivity and specificity of ctHPVDNA for detecting HPV+HNSCC was 98.4% and 98.6%. Sensitivity and specificity of a composite non-invasive diagnostic using ctHPVDNA and imaging/physical exam were 95.1% and 98.6%. Diagnostic accuracy of this non-invasive approach was significantly higher than standard of care (Youden index 0.937 vs 0.707, p=0.0006). Costs of non-invasive diagnostic were 36-38% less than standard clinical work up and the median Diagnostic Interval was 26 days less. CONCLUSIONS A non-invasive diagnostic approach for HPV+HNSCC demonstrated improved accuracy, reduced cost and a shorter time to diagnosis compared to standard clinical workup and could be a viable alternative in the future.
Collapse
Affiliation(s)
| | - Connor J O'Boyle
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear
| | | | - Natalia Queenan
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear
| | | | - Jarrod Stein
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear, Massachusetts General Hospital, Harvard Medical School, Broad Institute
| | - Julia Thierauf
- Department of Otolaryngology, Head and Neck Surgery, 1985
| | | | | | - Simon K Perry
- Department of Pathology, Massachusetts General Hospital
| | - Adam Z Bard
- Department of Pathology, Massachusetts General Hospital
| | - Wei Wang
- 6. Departments of Medicine and Neurology, Brigham and Women's Hospital
| | - Daniel G Deschler
- Otology and Laryngology, Massachusetts Eye and Ear Infirmary and Harvard Medical School
| | - Kevin S Emerick
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear, Massachusetts General Hospital, Harvard Medical School, Broad Institute
| | - Mark A Varvares
- Department of Otolaryngology, Massachusetts Eye and Ear Infirmary,, Harvard Medical School
| | - Jong C Park
- Hematology and Oncology, Massachusetts General Hospital
| | - John R Clark
- Hematology and Oncology, Massachusetts General Hospital/Harvard Medical School
| | - Annie W Chan
- Radiation Oncology, Massachusetts General Hospital
| | | | - Osamu Sakai
- Department or Radiology, Boston Medical Center
| | | | | | - Lori J Wirth
- Department of Medicine, Massachusetts General Hospital
| | | | | | - Jeremy D Richmon
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear, Massachusetts General Hospital, Harvard Medical School, Broad Institute
| | - Daniel L Faden
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear, Massachusetts General Hospital, Harvard Medical School, Broad Institute
| |
Collapse
|
17
|
Ito K, Muraoka H, Hirahara N, Sawada E, Hirohata S, Otsuka K, Okada S, Kaneda T. Quantitative assessment of mandibular bone marrow using computed tomography texture analysis for detect stage 0 medication-related osteonecrosis of the jaw. Eur J Radiol 2021; 145:110030. [PMID: 34798536 DOI: 10.1016/j.ejrad.2021.110030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/07/2021] [Accepted: 11/09/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication of treatment with bisphosphonates or antiangiogenic inhibitors. MRONJ has four stages (0-3); however, stage 0 MRONJ is difficult to detect using computed tomography (CT). This study aimed to quantitatively assess the mandibular bone marrow using texture analysis to detect stage 0 MRONJ from CT images. METHODS This retrospective study included 25 patients with stage 0 MRONJ who had a history of treatment with bisphosphonates and underwent CT and magnetic resonance imaging (MRI). The mandibular bone marrow with abnormal signals (T1-weighted imaging: low, T2-weighted imaging: low or high, short-tau inversion recovery: high) on MRI, and no qualitative characteristic CT and oral findings indicative of osteonecrosis (exposed bone, sequestrum, periosteal reaction, and osteolysis) was identified as 0 MRONJ. Texture features of the bone marrow of the mandible with MRONJ and the contralateral, normal mandibular bone marrow were extracted using an open-access software, namely, LIFEx. The volumes of interest (VOIs) were manually placed on CT images by tracing the bilateral mandibular bone marrow regions, excluding the teeth, mandibular canal, and cortical bone. Thirty-seven texture features were extracted from each VOI. RESULTS Six gray-level run length matrix features and four gray-level zone length matrix features exhibited significant differences between mandibular bone marrow with and without MRONJ. CONCLUSIONS CT was able to quantitatively assess texture features of normal mandibular bone marrow and that with MRONJ. Texture analysis may be useful as a new method for detecting stage 0 MRONJ using CT.
Collapse
Affiliation(s)
- Kotaro Ito
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan.
| | - Hirotaka Muraoka
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Naohisa Hirahara
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Eri Sawada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Shoya Hirohata
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Kohei Otsuka
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Shunya Okada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Takashi Kaneda
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| |
Collapse
|
18
|
Yu B, Huang C, Xu J, Liu S, Guan Y, Li T, Zheng X, Ding J. Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images. BMC Oral Health 2021; 21:585. [PMID: 34798867 PMCID: PMC8603498 DOI: 10.1186/s12903-021-01947-9] [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: 05/25/2021] [Accepted: 11/03/2021] [Indexed: 12/24/2022] Open
Abstract
Background Tongue squamous cell carcinoma (TSCC) is one of the most difficult malignancies to control. It displays particular and aggressive behaviour even at an early stage. The purpose of this paper is to explore the value of radiomics based on magnetic resonance fat-suppressed T2-weighted images in predicting the degree of pathological differentiation of TSCC. Methods Retrospective analysis of 127 patients with TSCC who were randomly divided into a primary cohort and a test cohort, including well-differentiated, moderately differentiated and poorly differentiated. The tumour regions were manually labelled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method. The model was established by the logistic regression classifier using a 5-fold cross-validation method, applied to all data and evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results In total, 1132 features were extracted, and seven features were selected for modelling. The AUC in the logistic regression model for well-differentiated TSCC was 0.90 with specificity and precision values of 0.92 and 0.78, respectively, and the sensitivity for poorly differentiated TSCC was 0.74. Conclusions The MRI-based radiomics signature could discriminate between well-differentiated, moderately differentiated and poorly differentiated TSCC and might be used as a biomarker for preoperative grading.
Collapse
Affiliation(s)
- Baoting Yu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, No. 829 of Xinmin Street, Chaoyang District, Beijing, 100080, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, No. 829 of Xinmin Street, Chaoyang District, Beijing, 100080, China
| | - Shuo Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China
| | - Yuyao Guan
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China
| | - Tong Li
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China
| | - Xuewei Zheng
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China
| | - Jun Ding
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, China. .,Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, No. 829 of Xinmin Street, Chaoyang District, Beijing, 100080, China.
| |
Collapse
|
19
|
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.
Collapse
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
| |
Collapse
|
20
|
Ito K, Muraoka H, Hirahara N, Sawada E, Tokunaga S, Kaneda T. Quantitative assessment of the parotid gland using computed tomography texture analysis to detect parotid sialadenitis. Oral Surg Oral Med Oral Pathol Oral Radiol 2021; 133:574-581. [PMID: 34953759 DOI: 10.1016/j.oooo.2021.10.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/26/2021] [Accepted: 10/30/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVE We aimed to quantitatively assess the parotid gland by using computed tomography (CT) texture analysis to detect parotid sialadenitis (PS). STUDY DESIGN This retrospective case-control study included 43 patients with PS who underwent CT and magnetic resonance imaging (MRI). Parotid glands with an abnormal signal (STIR: High) on MRI were identified as showing PS. Patients with parotid gland tumors, bilateral PS, marked fatty degeneration, and severe artifacts on CT were excluded. The texture features of parotid glands with PS and the contralateral normal parotid glands were analyzed using the open-access software LIFEx. The regions of interest were manually placed by tracing contours of both parotid glands on CT images. The results were tested with the paired t-test (or Wilcoxon rank-sum test when appropriate). Receiver operating characteristic (ROC) curve analysis was performed to assess the ability of texture features to predict PS. RESULTS Six gray level run length matrix features, 2 neighborhood gray level difference matrix features, and 5 gray level zone length matrix features displayed significant differences between PS and normal glands (P ≤ .047). ROC curve analysis showed acceptable accuracy in 4 texture features. CONCLUSIONS CT texture analysis allowed quantitative assessment of parotid glands and may have the potential to detect PS.
Collapse
Affiliation(s)
- Kotaro Ito
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan.
| | - Hirotaka Muraoka
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Naohisa Hirahara
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Eri Sawada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Satoshi Tokunaga
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Takashi Kaneda
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| |
Collapse
|
21
|
Marcu LG, Marcu DC. Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy. J Pers Med 2021; 11:jpm11111094. [PMID: 34834445 PMCID: PMC8625829 DOI: 10.3390/jpm11111094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 12/12/2022] Open
Abstract
Chemoradiotherapy remains the most common management of locally advanced head and neck cancer. While both treatment components have greatly developed over the years, the quality of life and long-term survival of patients undergoing treatment for head and neck malignancies are still poor. Research in head and neck oncology is equally focused on the improvement of tumour response to treatment and on the limitation of normal tissue toxicity. In this regard, personalised therapy through a multi-omics approach targeting patient management from diagnosis to treatment shows promising results. The aim of this paper is to discuss the latest results regarding the personalised approach to chemoradiotherapy of head and neck cancer by gathering the findings of the newest omics, involving radiotherapy (dosiomics), chemotherapy (pharmacomics), and medical imaging for treatment monitoring (radiomics). The incorporation of these omics into head and neck cancer management offers multiple viewpoints to treatment that represent the foundation of personalised therapy.
Collapse
Affiliation(s)
- Loredana G. Marcu
- Faculty of Informatics & Science, University of Oradea, 410087 Oradea, Romania
- Cancer Research Institute, University of South Australia, Adelaide, SA 5001, Australia
- Correspondence:
| | - David C. Marcu
- Faculty of Electrical Engineering & Information Technology, University of Oradea, 410087 Oradea, Romania;
| |
Collapse
|
22
|
Blomain ES, Moding EJ. Liquid Biopsies for Molecular Biology-Based Radiotherapy. Int J Mol Sci 2021; 22:11267. [PMID: 34681925 PMCID: PMC8538046 DOI: 10.3390/ijms222011267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 11/29/2022] Open
Abstract
Molecular alterations drive cancer initiation and evolution during development and in response to therapy. Radiotherapy is one of the most commonly employed cancer treatment modalities, but radiobiologic approaches for personalizing therapy based on tumor biology and individual risks remain to be defined. In recent years, analysis of circulating nucleic acids has emerged as a non-invasive approach to leverage tumor molecular abnormalities as biomarkers of prognosis and treatment response. Here, we evaluate the roles of circulating tumor DNA and related analyses as powerful tools for precision radiotherapy. We highlight emerging work advancing liquid biopsies beyond biomarker studies into translational research investigating tumor clonal evolution and acquired resistance.
Collapse
Affiliation(s)
- Erik S. Blomain
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Everett J. Moding
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA;
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| |
Collapse
|
23
|
Piludu F, Marzi S, Gangemi E, Farneti A, Marucci L, Venuti A, Benevolo M, Pichi B, Pellini R, Sperati F, Covello R, Sanguineti G, Vidiri A. Multiparametric MRI Evaluation of Oropharyngeal Squamous Cell Carcinoma. A Mono-Institutional Study. J Clin Med 2021; 10:jcm10173865. [PMID: 34501313 PMCID: PMC8432241 DOI: 10.3390/jcm10173865] [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: 07/15/2021] [Revised: 08/10/2021] [Accepted: 08/20/2021] [Indexed: 12/30/2022] Open
Abstract
The aim of this paper is to define the pre-treatment radiological characteristics of oropharyngeal squamous cell carcinoma (OPSCC) using morphological and non-morphological magnetic resonance imaging (MRI), based on HPV status, in a single-institution cohort. In total, 100 patients affected by OPSCC were prospectively enrolled in the present study. All patients underwent 1.5T MR with standard sequences, including diffusion-weighted imaging with and intravoxel incoherent motion (IVIM-DWI) technique and a dynamic contrast-enhanced (DCE) MRI. For all patients, human papillomavirus (HPV) status was available. No statistically significant differences in the volume of primary tumors (PTs) and lymph nodes (LNs) were observed based on HPV status. When comparing the two patient groups, no significant differences were found for the PT radiologic characteristics (presence of well-defined borders, exophytic growth, ulceration, and necrosis) and LN morphology (solid/cystic/necrotic). Tumor subsite, smoking status, and alcohol intake significantly differed based on HPV status, as well as ADC and Dt values of both PTs and LNs. We detected no significant difference in DCE-MRI parameters by HPV status. Based on a multivariate logistic regression model, the combination of clinical factors, such as tumor subsite and alcohol habits, with the perfusion-free diffusion coefficient Dt of LNs, may help to accurately discriminate OPSCC by HPV status.
Collapse
Affiliation(s)
- Francesca Piludu
- Department of Radiology and Diagnostic Imaging, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy; (F.P.); (E.G.)
| | - Simona Marzi
- Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;
| | - Emma Gangemi
- Department of Radiology and Diagnostic Imaging, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy; (F.P.); (E.G.)
- Center for Integrated Research, Departmental Faculty of Medicine and Surgery, University Campus Bio-Medico of Rome, Via Álvaro del Portillo, 33, 00128 Rome, Italy
| | - Alessia Farneti
- Department of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy; (A.F.); (L.M.); (G.S.)
| | - Laura Marucci
- Department of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy; (A.F.); (L.M.); (G.S.)
| | - Aldo Venuti
- HPV Unit (UOSD), Department of Tumor Immunology and Immunotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;
| | - Maria Benevolo
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy; (M.B.); (R.C.)
| | - Barbara Pichi
- Department of Otolaryngology and Head and Neck Surgery, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy; (B.P.); (R.P.)
| | - Raul Pellini
- Department of Otolaryngology and Head and Neck Surgery, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy; (B.P.); (R.P.)
| | - Francesca Sperati
- Biostatistics-Scientific Direction, IRCCS San Gallicano Dermatological Institute, Via Elio Chianesi 53, 00144 Rome, Italy;
| | - Renato Covello
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy; (M.B.); (R.C.)
| | - Giuseppe Sanguineti
- Department of Radiotherapy, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy; (A.F.); (L.M.); (G.S.)
| | - Antonello Vidiri
- Department of Radiology and Diagnostic Imaging, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy; (F.P.); (E.G.)
- Correspondence: ; Tel.: +39-335-547-6057
| |
Collapse
|
24
|
Ito K, Kondo T, Andreu-Arasa VC, Li B, Hirahara N, Muraoka H, Sakai O, Kaneda T. Quantitative assessment of the maxillary sinusitis using computed tomography texture analysis: odontogenic vs non-odontogenic etiology. Oral Radiol 2021; 38:315-324. [PMID: 34327595 DOI: 10.1007/s11282-021-00558-y] [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: 05/18/2021] [Accepted: 07/25/2021] [Indexed: 08/29/2023]
Abstract
OBJECTIVES The purpose of this study was to investigate computed tomography (CT) texture features of mucosal thickening of maxillary sinus mucosa to differentiate odontogenic maxillary sinusitis (OMS) from non-odontogenic maxillary sinusitis (NOMS). METHODS Eighteen OMS patients and age- and gender-matched 18 NOMS patients who underwent sinus CT were retrospectively reviewed. OMS patients were identified by histopathological examination of tissues excised at surgery combined with CT imaging findings. Patients with mucosal thickening in the maxillary sinus without apical periodontitis or advanced periodontal bone loss near the maxillary sinus on CT were defined as NOMS. Patients with thin mucosal thickening (< 10 mm), cyst, tumor, post-operative deformity, severe metal artifact precluding visualization of the maxillary sinus, and age younger than 20 years were excluded. CT texture features of the mucosal thickening were analyzed using an in-house developed Matlab-based texture analysis program. Forty-five texture features were extracted from each segmented volume. The results were tested with the Mann-Whitney U test. RESULTS Six histogram features (mean, median, standard deviation, entropy, geometric mean, harmonic mean) and two gray-level co-occurrence matrix features (entropy, correlation) showed significant differences between OMS and NOMS patients. CONCLUSIONS CT texture analysis revealed the quantitative differences between OMS and NOMS. The texture features can serve as a quantitative indicator of maxillary sinusitis to differentiate between OMS and NOMS and help prevent incorrect treatment choices.
Collapse
Affiliation(s)
- Kotaro Ito
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan.
| | - Takumi Kondo
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Baojun Li
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Naohisa Hirahara
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Hirotaka Muraoka
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA.,Department of Otolaryngology, Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA.,Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Takashi Kaneda
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| |
Collapse
|
25
|
Bruixola G, Remacha E, Jiménez-Pastor A, Dualde D, Viala A, Montón JV, Ibarrola-Villava M, Alberich-Bayarri Á, Cervantes A. Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges. Cancer Treat Rev 2021; 99:102263. [PMID: 34343892 DOI: 10.1016/j.ctrv.2021.102263] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 07/06/2021] [Accepted: 07/23/2021] [Indexed: 12/12/2022]
Abstract
The application of imaging biomarkers in oncology is still in its infancy, but with the expansion of radiomics and radiogenomics a revolution is expected in this field. This may be of special interest in head and neck cancer, since it can promote precision medicine and personalization of treatment by overcoming several intrinsic obstacles in this pathology. Our goal is to provide the medical oncologist with the basis to approach these disciplines and appreciate their main uses in clinical research and clinical practice in the medium term. Aligned with this objective we analyzed the most relevant studies in the field, also highlighting novel opportunities and current challenges.
Collapse
Affiliation(s)
- Gema Bruixola
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Elena Remacha
- Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain
| | - Ana Jiménez-Pastor
- Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain
| | - Delfina Dualde
- Department of Radiology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Alba Viala
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Jose Vicente Montón
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Maider Ibarrola-Villava
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Andrés Cervantes
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain.
| |
Collapse
|
26
|
Zhang MH, Hasse A, Carroll T, Pearson AT, Cipriani NA, Ginat DT. Differentiating low and high grade mucoepidermoid carcinoma of the salivary glands using CT radiomics. Gland Surg 2021; 10:1646-1654. [PMID: 34164309 DOI: 10.21037/gs-20-830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background The purpose of this study is to determine if Haralick texture analysis on CT imaging of mucoepidermoid carcinomas (MEC) can differentiate low-grade and high-grade tumors. Methods A retrospective review of 18 patients with MEC of the salivary glands, corresponding CT imaging and pathology report was performed. Tumors were manually segmented and image analysis was performed to calculate radiomic features. Radiomic features were compared between low-grade and high-grade MEC. A multivariable logistic regression model and receiver operating characteristic analysis was performed. Results A total of 18 patients (mean age, 51, range 9-83 years, 8 men and 10 women) were included. Nine patients had low-grade pathology and nine patients had high-grade pathology. Of the 18 cases, 7 (39%) occurred in the parotid gland and 11 (61%) occurred in minor salivary glands. No individual feature was significantly different between low-grade and high-grade MEC. A logistic regression model including surface regularity, energy and information measure II of correlation was performed and was able to predict high-grade MEC accurately (sensitivity 89%, specificity 68%). The area under the receiver operating characteristic curve was 0.802. Conclusions High-grade MEC tend to have a low energy, high correlation texture as well as surface irregularity. Together, these three features may comprise a tumor phenotype that is able to predict high-grade pathology in MECs.
Collapse
Affiliation(s)
- Michael H Zhang
- Pritzker School of Medicine, The University of Chicago, Chicago IL, USA
| | - Adam Hasse
- Graduate Program in Medical Physics, The University of Chicago, Chicago, IL, USA
| | - Timothy Carroll
- Graduate Program in Medical Physics, The University of Chicago, Chicago, IL, USA
| | | | | | - Daniel T Ginat
- Department of Radiology, The University of Chicago, Chicago IL, USA
| |
Collapse
|
27
|
Radiomic Model Predicts Lymph Node Response to Induction Chemotherapy in Locally Advanced Head and Neck Cancer. Diagnostics (Basel) 2021; 11:diagnostics11040588. [PMID: 33806029 PMCID: PMC8064478 DOI: 10.3390/diagnostics11040588] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 12/24/2022] Open
Abstract
This study developed a pretreatment CT-based radiomic model of lymph node response to induction chemotherapy in locally advanced head and neck squamous cell carcinoma (HNSCC) patients. This was a single-center retrospective study of patients with locally advanced HPV+ HNSCC. Forty-one enlarged lymph nodes were found from 27 patients on pretreatment CT and were split into 3:1 training and testing cohorts. Ninety-three radiomic features were extracted. A radiomic model and a combined radiomic-clinical model predicting lymph node response to induction chemotherapy were developed using multivariable logistic regression. Median age was 57 years old, and 93% of patients were male. Post-treatment evaluation was 32 days after treatment, with a median reduction in lymph node volume of 66%. A three-feature radiomic model (minimum, skewness, and low gray level run emphasis) and a combined radiomic-clinical model were developed. The combined model performed the best, with AUC = 0.85 on the training cohort and AUC = 0.75 on the testing cohort. A pretreatment CT-based lymph node radiomic signature combined with clinical parameters was able to predict nodal response to induction chemotherapy for patients with locally advanced HNSCC.
Collapse
|
28
|
Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma. Sci Rep 2021; 11:4679. [PMID: 33633160 PMCID: PMC7907098 DOI: 10.1038/s41598-021-84048-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 02/11/2021] [Indexed: 01/06/2023] Open
Abstract
The purpose of this study was to examine differences in texture features between olfactory neuroblastoma (ONB) and sinonasal squamous cell carcinoma (SCC) on contrast-enhanced CT (CECT) images, and to evaluate the predictive accuracy of texture analysis compared to radiologists’ interpretations. Forty-three patients with pathologically-diagnosed primary nasal and paranasal tumor (17 ONB and 26 SCC) were included. We extracted 42 texture features from tumor regions on CECT images obtained before treatment. In univariate analysis, each texture features were compared, with adjustment for multiple comparisons. In multivariate analysis, the elastic net was used to select useful texture features and to construct a texture-based prediction model with leave-one-out cross-validation. The prediction accuracy was compared with two radiologists’ visual interpretations. In univariate analysis, significant differences were observed for 28 of 42 texture features between ONB and SCC, with areas under the receiver operating characteristic curve between 0.68 and 0.91 (median: 0.80). In multivariate analysis, the elastic net model selected 18 texture features that contributed to differentiation. It tended to show slightly higher predictive accuracy than radiologists’ interpretations (86% and 74%, respectively; P = 0.096). In conclusion, several texture features contributed to differentiation of ONB from SCC, and the texture-based prediction model was considered useful.
Collapse
|
29
|
Ito K, Muraoka H, Hirahara N, Sawada E, Okada S, Kaneda T. Computed tomography texture analysis of mandibular condylar bone marrow in diabetes mellitus patients. Oral Radiol 2021; 37:693-699. [PMID: 33611771 DOI: 10.1007/s11282-021-00517-7] [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: 12/05/2020] [Accepted: 02/08/2021] [Indexed: 10/22/2022]
Abstract
OBJECTIVES Diabetes mellitus (DM) is associated with a broad range of complications, such as retinopathy, nephropathy, neuropathy, and cardiovascular disease. Therefore, predicting DM from head and neck images is a challenge for clinicians. The purpose of this study was to assess the mandibular condylar bone marrow in DM patients using computed tomography (CT) texture analysis. METHODS This retrospective study included 16 DM and age and sex matched 16 control patients (11 men, 5 women; mean age, 56.8 ± 14.4 years; range 31-78 years). Patients with Type I DM, prior history of taking bisphosphonates, osteoarthritis of the temporomandibular joint, and CT images with metal artifacts were excluded from this study. Bilateral mandibular condylar bone marrow was manually contoured on axial CT images. The presence or absence of DM is the primary predictor variable. Texture features of the region of interest was the outcome variable, that were analyzed using an open-access software, MaZda Ver.3.3. For each group, 20 features out of 279 parameters were selected with Fisher, probability of error and average correlation coefficient methods in MaZda. Bivariate statistics were computed with the Mann-Whitney U test and the P value was set at .05. RESULTS One histogram feature, 15 Gy level co-occurrence matrix features, and four gray level run length matrix features showed differences between the DM patients and non-DM patients (P < 0.05). CONCLUSIONS Several texture features of the condyle demonstrated differences between the DM and non-DM patients. CT texture analysis may potentially detect DM from the condylar bone marrow.
Collapse
Affiliation(s)
- Kotaro Ito
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan.
| | - Hirotaka Muraoka
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Naohisa Hirahara
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Eri Sawada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Shunya Okada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| | - Takashi Kaneda
- Department of Radiology, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho-Nishi, Matsudo, Chiba, 271-8587, Japan
| |
Collapse
|
30
|
Foy JJ, Shenouda M, Ramahi S, Armato S, Ginat DT. Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features. J Med Imaging (Bellingham) 2020; 7:064007. [PMID: 33409336 DOI: 10.1117/1.jmi.7.6.064007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 12/01/2020] [Indexed: 01/10/2023] Open
Abstract
Purpose: The goal of this study was to quantify the effects of iterative reconstruction on radiomics features of normal anatomic structures on head and neck computed tomography (CT) scans. Methods: Regions of interest (ROI) containing five different tissue types and an ROI containing only air were extracted from CT scans of the head and neck from 108 patients. Each scan was reconstructed using three different iDose 4 reconstruction levels (2, 4, and 6) in addition to bone, thin slice (1-mm slice thickness), and thin-bone reconstructions. From each ROI in all reconstructions, 142 radiomic features were calculated. For each of the six ROIs, features were compared between combinations of iDose levels (2v4, 4v6, and 2v6) with a threshold of α = 0.05 after correcting for multiple comparisons ( p < 0.00006 ). Features from iDose 4 - 2 reconstructions were also compared to bone, thin slice, and thin-bone reconstructions. Spearman's rank correlation coefficient, ρ , quantified the relative feature value agreement across iDose 4 reconstructions. Results: When comparing radiomics features across the three iDose 4 reconstruction levels, over half of all features reflected significant differences for all tissue types, while no features demonstrated significant differences when extracted from air ROIs. When assessing feature value agreement, at least 97% of features reflected excellent agreement ( ρ > 0.9 ) when comparing the three iDose levels for all ROIs. When comparing iDose 4 - 2 to bone, thin slice, and thin-bone reconstructions, more than half of all features demonstrated significant differences for all ROIs and 89 % of features reflected excellent agreement for all ROIs. Conclusion: Many radiomics features are dependent on the iterative reconstruction level, and the magnitude of this dependency is affected by the tissue from which features are extracted. For studies using images reconstructed using varying iDose 4 reconstruction levels, features robust to these should be used.
Collapse
Affiliation(s)
- Joseph J Foy
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Mena Shenouda
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Sahar Ramahi
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Samuel Armato
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Daniel Thomas Ginat
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| |
Collapse
|
31
|
Ren C, Li M, Zhang Y, Zhang S. Development and validation of a CT-texture analysis nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes. Cancer Imaging 2020; 20:86. [PMID: 33308325 PMCID: PMC7731456 DOI: 10.1186/s40644-020-00364-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 11/26/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Thymic epithelial tumors (TETs) are the most common primary tumors in the anterior mediastinum, which have considerable histologic heterogeneity. This study aimed to develop and validate a nomogram based on computed tomography (CT) and texture analysis (TA) for preoperatively predicting the pathological classifications for TET patients. METHODS Totally TET 172 patients confirmed by postoperative pathology between January 2011 to April 2019 were retrospectively analyzed and randomly divided into training (n = 120) and validation (n = 52) cohorts. Preoperative clinical factors, CT signs and texture features of each patient were analyzed, and prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and the DeLong test. The clinical application value of the models was determined via the decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and validated using the calibration plots. RESULTS Totally 87 patients with low-risk TET (LTET) (types A, AB, B1) and 85 patients with high-risk TET (HTET) (types B2, B3, C) were enrolled in this study. We separately constructed 4 prediction models for differentiating LTET from HTET using clinical, CT, texture features, and their combination. These 4 prediction models achieved AUCs of 0.66, 0.79, 0.82, 0.88 in the training cohort and 0.64, 0.82, 0.86, 0.94 in the validation cohort, respectively. The DeLong test and DCA showed that the Combined model, consisting of 2 CT signs and 2 texture parameters, held the highest predictive efficiency and clinical utility (p < 0.05). A prediction nomogram was subsequently developed using the 4 independently risk factors from the Combined model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions for differentiating TET classifications. CONCLUSION A prediction nomogram incorporating both the CT and texture parameters was constructed and validated in our study, which can be conveniently used for the preoperative individualized prediction of the simplified histologic subtypes in TET patients.
Collapse
Affiliation(s)
- Caiyue Ren
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China. .,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, 4365 Kangxin Road, Shanghai, 201315, China.
| | - Mingli Li
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China.,Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, 4365 Kangxin Road, Shanghai, 201315, China
| | - Yunyan Zhang
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, 4365 Kangxin Road, Shanghai, 201315, China.,Department of Radiology, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| |
Collapse
|
32
|
Ito K, Muraoka H, Hirahara N, Sawada E, Okada S, Kaneda T. Quantitative assessment of normal submandibular glands and submandibular sialadenitis using CT texture analysis: A retrospective study. Oral Surg Oral Med Oral Pathol Oral Radiol 2020; 132:112-117. [PMID: 33214092 DOI: 10.1016/j.oooo.2020.10.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 10/04/2020] [Accepted: 10/10/2020] [Indexed: 12/29/2022]
Abstract
OBJECTIVE The purpose of this study was to quantitatively assess normal submandibular glands and submandibular sialadenitis (SS) using computed tomography (CT) texture analysis as part of radiomics quantitative analysis. STUDY DESIGN In total, 31 patients with unilateral SS who underwent head and neck magnetic resonance imaging (MRI) and CT and were retrospectively reviewed. Submandibular glands with abnormal signals (STIR: high, T2-weighted image: high, T1-weighted image: low) on MRI were identified as SS. The radiomics features of the contralateral normal submandibular glands and SS were analyzed using an open-access software, MaZda Version 3.3. Sixteen radiomics features were selected with Fisher and probability of error and average correlation coefficient methods in MaZda from 279 original parameters calculated for each of the normal and SS glands. The results were statistically analyzed with the Wilcoxon rank sum test. RESULTS One gray-level co-occurrence matrix feature and 9 gray-level run length matrix features displayed significant differences between normal submandibular glands and glands with SS (P < .05). CONCLUSIONS CT texture analysis was able to quantitatively distinguish between normal and diseased submandibular glands. It therefore may have the potential to detect SS.
Collapse
Affiliation(s)
- Kotaro Ito
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan.
| | - Hirotaka Muraoka
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Naohisa Hirahara
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Eri Sawada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Shunya Okada
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| | - Takashi Kaneda
- Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan
| |
Collapse
|
33
|
Choi Y, Nam Y, Jang J, Shin NY, Ahn KJ, Kim BS, Lee YS, Kim MS. Prediction of Human Papillomavirus Status and Overall Survival in Patients with Untreated Oropharyngeal Squamous Cell Carcinoma: Development and Validation of CT-Based Radiomics. AJNR Am J Neuroradiol 2020; 41:1897-1904. [PMID: 32943420 DOI: 10.3174/ajnr.a6756] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 07/03/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE Human papillomavirus is a prognostic marker for oropharyngeal squamous cell carcinoma. We aimed to determine the value of CT-based radiomics for predicting the human papillomavirus status and overall survival in patients with oropharyngeal squamous cell carcinoma. MATERIALS AND METHODS Eighty-six patients with oropharyngeal squamous cell carcinoma were retrospectively collected and grouped into training (n = 61) and test (n = 25) sets. For human papillomavirus status and overall survival prediction, radiomics features were selected via a random forest-based algorithm and Cox regression analysis, respectively. Relevant features were used to build multivariate Cox regression models and calculate the radiomics score. Human papillomavirus status and overall survival prediction were assessed via the area under the curve and concordance index, respectively. The models were validated in the test and The Cancer Imaging Archive cohorts (n = 78). RESULTS For prediction of human papillomavirus status, radiomics features yielded areas under the curve of 0.865, 0.747, and 0.834 in the training, test, and validation sets, respectively. In the univariate Cox regression, the human papillomavirus status (positive: hazard ratio, 0.257; 95% CI, 0.09-0.7; P = .008), T-stage (≥III: hazard ratio, 3.66; 95% CI, 1.34-9.99; P = .011), and radiomics score (high-risk: hazard ratio, 3.72; 95% CI, 1.21-11.46; P = .022) were associated with overall survival. The addition of the radiomics score to the clinical Cox model increased the concordance index from 0.702 to 0.733 (P = .01). Validation yielded concordance indices of 0.866 and 0.720. CONCLUSIONS CT-based radiomics may be useful in predicting human papillomavirus status and overall survival in patients with oropharyngeal squamous cell carcinoma.
Collapse
Affiliation(s)
- Y Choi
- Department of Radiology (Y.C., Y.N., J.J., N.-Y.S, K.-J.A., B.-S.K.), Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Y Nam
- Department of Radiology (Y.C., Y.N., J.J., N.-Y.S, K.-J.A., B.-S.K.), Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Biomedical Engineering (Y.N.), Hankuk University of Foreign Studies, Yongin-Si, Gyeonggi-do, Republic of Korea
| | - J Jang
- Department of Radiology (Y.C., Y.N., J.J., N.-Y.S, K.-J.A., B.-S.K.), Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - N-Y Shin
- Department of Radiology (Y.C., Y.N., J.J., N.-Y.S, K.-J.A., B.-S.K.), Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - K-J Ahn
- Department of Radiology (Y.C., Y.N., J.J., N.-Y.S, K.-J.A., B.-S.K.), Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - B-S Kim
- Department of Radiology (Y.C., Y.N., J.J., N.-Y.S, K.-J.A., B.-S.K.), Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Y-S Lee
- Department of Hospital Pathology (Y.-S.L.), Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - M-S Kim
- Department of Otolaryngology-Head and Neck Surgery (M.S.K.), Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| |
Collapse
|
34
|
Sohn B, Choi YS, Ahn SS, Kim H, Han K, Lee SK, Kim J. Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI. Laryngoscope 2020; 131:E851-E856. [PMID: 33070337 DOI: 10.1002/lary.28889] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 05/26/2020] [Accepted: 06/08/2020] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status. STUDY DESIGN Retrospective cohort study. METHODS Pretreatment MRI data from 62 consecutive patients with oropharyngeal SCC were retrospectively reviewed, and chronologically allocated to training (n = 43) and test sets (n = 19). Enhancing tumors were semi-automatically segmented on each slice of the postcontrast T1WI to span the entire tumor volume, after registration of T2WI to postcontrast T1WI; 170 radiomic features were extracted from the entire tumor volume. Relevant features were selected and radiomics models were trained using least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation, after subsampling of training sets using synthetic minority over-sampling technique to mitigate data imbalance. The selected features, weighted by their respective coefficients, were combined linearly to yield a radiomics score. The diagnostic performance of the radiomic score was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS Six radiomic features, which revealed strong association with HPV status of oropharyngeal SCC, were selected using LASSO. The radiomics model yielded excellent performance on the training set (AUC, 0.982 [95% CI, 0.942-1.000]) and moderate performance on the test set (AUC, 0.744 [95% CI, 0.496-0.991]) for differentiating oropharyngeal SCC according to HPV status. CONCLUSIONS Radiomics-based MRI phenotyping differentiates oropharyngeal SCC according to HPV status, and thus, is a potential imaging biomarker. LEVEL OF EVIDENCE 3 Laryngoscope, 131:E851-E856, 2021.
Collapse
Affiliation(s)
- Beomseok Sohn
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Yoon Seong Choi
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Hwiyoung Kim
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| |
Collapse
|
35
|
Machine learning–based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation. Eur Radiol 2020; 30:6858-6866. [DOI: 10.1007/s00330-020-07011-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/29/2020] [Accepted: 06/04/2020] [Indexed: 01/11/2023]
|
36
|
Radiomic biomarkers for head and neck squamous cell carcinoma. Strahlenther Onkol 2020; 196:868-878. [PMID: 32495038 DOI: 10.1007/s00066-020-01638-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 05/13/2020] [Indexed: 12/22/2022]
Abstract
Tumor heterogeneity is a well-known prognostic factor in head and neck squamous cell carcinoma (HNSCC). A major limitation of tissue- and blood-derived tumor markers is the lack of spatial resolution to image tumor heterogeneity. Tissue markers derived from tumor biopsies usually represent only a small tumor subregion at a single timepoint and are therefore often not representative of the tumors' biology or the biological alterations during and after treatment. Similarly, liquid biopsies give an overall picture of the tumors' secreted factors but completely lack any spatial resolution. Radiomics has the potential to give complete three-dimensional information about the tumor. We conducted a comprehensive literature search to assess the correlation of radiomics to tumor biology and treatment outcome in HNSCC and to assess current limitations of the radiomic biomarkers. In total, 25 studies that explored the ability of radiomics to predict tumor biology and phenotype in HNSCC and 28 studies that explored radiomics to predict post-treatment events were identified. Out of these 53 studies, only three failed to show a significant correlation. The major technical challenges are currently artifacts due to metal implants, non-standardized contrast injection, and delineation uncertainties. All studies to date were retrospective and none of the above-mentioned radiomics signatures have been validated in an independent cohort using an independent software implementation, which shows that transferability due to the numerous technical challenges is currently a major limitation. However, radiomics is a very young field and these studies hopefully pave the way for clinical implementation of radiomics for HNSCC in the future.
Collapse
|
37
|
Mukherjee P, Cintra M, Huang C, Zhou M, Zhu S, Colevas AD, Fischbein N, Gevaert O. CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma. Radiol Imaging Cancer 2020; 2:e190039. [PMID: 32550599 DOI: 10.1148/rycan.2020190039] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 01/08/2020] [Accepted: 01/22/2020] [Indexed: 12/15/2022]
Abstract
Purpose To determine the performance of CT-based radiomic features for noninvasive prediction of histopathologic features of tumor grade, extracapsular spread, perineural invasion, lymphovascular invasion, and human papillomavirus status in head and neck squamous cell carcinoma (HNSCC). Materials and Methods In this retrospective study, which was approved by the local institutional ethics committee, CT images and clinical data from patients with pathologically proven HNSCC from The Cancer Genome Atlas (n = 113) and an institutional test cohort (n = 71) were analyzed. A machine learning model was trained with 2131 extracted radiomic features to predict tumor histopathologic characteristics. In the model, principal component analysis was used for dimensionality reduction, and regularized regression was used for classification. Results The trained radiomic model demonstrated moderate capability of predicting HNSCC features. In the training cohort and the test cohort, the model achieved a mean area under the receiver operating characteristic curve (AUC) of 0.75 (95% confidence interval [CI]: 0.68, 0.81) and 0.66 (95% CI: 0.45, 0.84), respectively, for tumor grade; a mean AUC of 0.64 (95% CI: 0.55, 0.62) and 0.70 (95% CI: 0.47, 0.89), respectively, for perineural invasion; a mean AUC of 0.69 (95% CI: 0.56, 0.81) and 0.65 (95% CI: 0.38, 0.87), respectively, for lymphovascular invasion; a mean AUC of 0.77 (95% CI: 0.65, 0.88) and 0.67 (95% CI: 0.15, 0.80), respectively, for extracapsular spread; and a mean AUC of 0.71 (95% CI: 0.29, 1.0) and 0.80 (95% CI: 0.65, 0.92), respectively, for human papillomavirus status. Conclusion Radiomic CT models have the potential to predict characteristics typically identified on pathologic assessment of HNSCC.Supplemental material is available for this article.© RSNA, 2020.
Collapse
Affiliation(s)
- Pritam Mukherjee
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Murilo Cintra
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Chao Huang
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Mu Zhou
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Shankuan Zhu
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - A Dimitrios Colevas
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Nancy Fischbein
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| | - Olivier Gevaert
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479
| |
Collapse
|
38
|
Haider SP, Burtness B, Yarbrough WG, Payabvash S. Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas. CANCERS OF THE HEAD & NECK 2020; 5:6. [PMID: 32391171 PMCID: PMC7197186 DOI: 10.1186/s41199-020-00053-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/09/2020] [Indexed: 12/15/2022]
Abstract
Recent advancements in computational power, machine learning, and artificial intelligence technology have enabled automated evaluation of medical images to generate quantitative diagnostic and prognostic biomarkers. Such objective biomarkers are readily available and have the potential to improve personalized treatment, precision medicine, and patient selection for clinical trials. In this article, we explore the merits of the most recent addition to the “-omics” concept for the broader field of head and neck cancer – “Radiomics”. This review discusses radiomics studies focused on (molecular) characterization, classification, prognostication and treatment guidance for head and neck squamous cell carcinomas (HNSCC). We review the underlying hypothesis, general concept and typical workflow of radiomic analysis, and elaborate on current and future challenges to be addressed before routine clinical application.
Collapse
Affiliation(s)
- Stefan P Haider
- 1Department of Radiology and Biomedical Imaging, Division of Neuroradiology, Yale School of Medicine, New Haven, CT USA.,2Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians University of Munich, Munich, Germany
| | - Barbara Burtness
- 3Department of Internal Medicine, Division of Medical Oncology, Yale School of Medicine, New Haven, CT USA
| | - Wendell G Yarbrough
- 4Department of Otolaryngology/Head and Neck Surgery, University of North Carolina School of Medicine, Chapel Hill, NC USA
| | - Seyedmehdi Payabvash
- 1Department of Radiology and Biomedical Imaging, Division of Neuroradiology, Yale School of Medicine, New Haven, CT USA
| |
Collapse
|
39
|
Qiu Q, Duan J, Yin Y. Radiomics in radiotherapy: Applications and future challenges. PRECISION RADIATION ONCOLOGY 2020. [DOI: 10.1002/pro6.1087] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
- Qingtao Qiu
- Department of Radiation OncologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical Sciences Jinan PR China
| | - Jinghao Duan
- Department of Radiation OncologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical Sciences Jinan PR China
| | - Yong Yin
- Department of Radiation OncologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical Sciences Jinan PR China
| |
Collapse
|
40
|
Ye J, Luo J, Xu S, Wu W. One-slice CT image based kernelized radiomics model for the prediction of low/mid-grade and high-grade HNSCC. Comput Med Imaging Graph 2019; 80:101675. [PMID: 31945637 DOI: 10.1016/j.compmedimag.2019.101675] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 10/18/2019] [Accepted: 10/24/2019] [Indexed: 01/02/2023]
Abstract
An accurate grade prediction can help to appropriate treatment strategy and effective diagnosis to Head and neck squamous cell carcinoma (HNSCC). Radiomics has been studied for the prediction of carcinoma characteristics in medical images. The success of previous researches in radiomics is attributed to the availability of annotated all-slice medical images. However, it is very challenging to annotate all slices, as annotating biomedical images is not only tedious, laborious, and time consuming, but also demanding of costly, specialty-oriented skills, which are not easily accessible. To address this problem, this paper presents a model to integrate radiomics and kernelized dimension reduction into a single framework, which maps handcrafted radiomics features to a kernelized space where they are linearly separable and then reduces the dimension of features through principal component analysis. Three methods including baseline radiomics models, proposed kernelized model and convolutional neural network (CNN) model were compared in experiments. Results suggested proposed kernelized model best fit in one-slice data. We reached AUC of 95.91 % on self-made one-slice dataset, 67.33 % in predicting localregional recurrence on H&N dataset and 64.33 % on H&N1 dataset. While all other models were <76 %, <65 %, and <62 %. Though CNN model reached an incredible performance when predicting distant metastasis on H&N (AUC 0.88), model faced serious problem of overfitting in small datasets. When changing all-slice data to one-slice on both H&N and H&N1, proposed model suffered less loss on AUC (<1.3 %) than any other models (>3 %). These proved our proposed model is efficient to deal with the one-slice problem and makes using one-slice data to reduce annotation cost practical. This is attributed to the several advantages derived from the proposed kernelized radiomics model, including (1) the prior radiomics features reduced the demanding of huge amount of data and avoided overfitting; (2) the kernelized method mined the potential information contributed to predict; (3) generating principal components in kernelized features reduced redundant features.
Collapse
Affiliation(s)
- Junyong Ye
- Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Shapingba, Chongqing, China.
| | - Jin Luo
- Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Shapingba, Chongqing, China
| | - Shengsheng Xu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wenli Wu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| |
Collapse
|
41
|
Moan JM, Amdal CD, Malinen E, Svestad JG, Bogsrud TV, Dale E. The prognostic role of 18F-fluorodeoxyglucose PET in head and neck cancer depends on HPV status. Radiother Oncol 2019; 140:54-61. [DOI: 10.1016/j.radonc.2019.05.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 05/13/2019] [Accepted: 05/15/2019] [Indexed: 11/25/2022]
|
42
|
Vidiri A, Marzi S, Gangemi E, Benevolo M, Rollo F, Farneti A, Marucci L, Spasiano F, Sperati F, Di Giuliano F, Pellini R, Sanguineti G. Intravoxel incoherent motion diffusion-weighted imaging for oropharyngeal squamous cell carcinoma: Correlation with human papillomavirus Status. Eur J Radiol 2019; 119:108640. [DOI: 10.1016/j.ejrad.2019.08.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 07/17/2019] [Accepted: 08/11/2019] [Indexed: 01/04/2023]
|
43
|
Oda M, Staziaki PV, Qureshi MM, Andreu-Arasa VC, Li B, Takumi K, Chapman MN, Wang A, Salama AR, Sakai O. Using CT texture analysis to differentiate cystic and cystic-appearing odontogenic lesions. Eur J Radiol 2019; 120:108654. [PMID: 31539792 DOI: 10.1016/j.ejrad.2019.108654] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 08/16/2019] [Accepted: 08/26/2019] [Indexed: 12/19/2022]
Abstract
PURPOSE Cystic and cystic-appearing odontogenic lesions of the jaw may appear similar on CT imaging. Accurate diagnosis is often difficult although the relationship of the lesion to the tooth root or crown may offer a clue to the etiology. The purpose of this study was to evaluate CT texture analysis as an aid in differentiating cystic and cystic-appearing odontogenic lesions of the jaw. METHODS This was an IRB-approved retrospective study including 42 pathology-proven dentigerous cysts, 37 odontogenic keratocysts, and 19 ameloblastomas. Each lesion was manually segmented on axial CT images, and textural features were analyzed using an in-house-developed Matlab-based texture analysis program that extracted 47 texture features from each segmented volume. Statistical analysis was performed comparing all pairs of the three types of lesions. RESULTS Pairwise analysis revealed that nine histogram features, one GLCM feature, three GLRL features, two Laws features, four GLGM features and two Chi-square features showed significant differences between dentigerous cysts and odontogenic keratocysts. Four histogram features and one Chi-square feature showed significant differences between odontogenic keratocysts and ameloblastomas. Two histogram features showed significant differences between dentigerous cysts and ameloblastomas. CONCLUSIONS CT texture analysis may be useful as a noninvasive method to obtain additional quantitative information to differentiate cystic and cystic-appearing odontogenic lesions of the jaw.
Collapse
Affiliation(s)
- Masafumi Oda
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Division of Oral and Maxillofacial Radiology, Kyushu Dental University, Kitakyushu, Fukuoka, Japan
| | - Pedro V Staziaki
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Muhammad M Qureshi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, United States
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Baojun Li
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Koji Takumi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Margaret N Chapman
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Albert Wang
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Andrew R Salama
- Deparment of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, United States; Department of Oral & Maxillofacial Surgery, Boston Medical Center, Boston University Henry M. Goldman School of Dental Medicine, United States
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, United States.
| |
Collapse
|
44
|
Wu W, Ye J, Wang Q, Luo J, Xu S. CT-Based Radiomics Signature for the Preoperative Discrimination Between Head and Neck Squamous Cell Carcinoma Grades. Front Oncol 2019; 9:821. [PMID: 31544063 PMCID: PMC6729100 DOI: 10.3389/fonc.2019.00821] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 08/09/2019] [Indexed: 12/15/2022] Open
Abstract
Background: Radiomics has been widely used to non-invasively mine quantitative information from medical images and could potentially predict tumor phenotypes. Pathologic grade is considered a predictive prognostic factor for head and neck squamous cell carcinoma (HNSCC) patients. A preoperative histological assessment can be important in the clinical management of patients. We applied radiomics analysis to devise non-invasive biomarkers and accurately differentiate between well-differentiated (WD) and moderately differentiated (MD) and poorly differentiated (PD) HNSCC. Methods: This study involved 206 consecutive HNSCC patients (training cohort: n = 137; testing cohort: n = 69). In total, we extracted 670 radiomics features from contrast-enhanced computed tomography (CT) images. Radiomics signatures were constructed with a kernel principal component analysis (KPCA), random forest classifier and a variance-threshold (VT) selection. The associations between the radiomics signatures and HNSCC histological grades were investigated. A clinical model and combined model were also constructed. Areas under the receiver operating characteristic curves (AUCs) were applied to evaluate the performances of the three models. Results: In total, 670 features were selected by the KPCA and random forest methods from the CT images. The radiomics signatures had a good performance in discriminating between the two cohorts of HNSCC grades, with an AUC of 0.96 and an accuracy of 0.92. The specificity, accuracy, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of the abovementioned method with a VT selection for determining HNSCC grades were 0.83, 0.92, 0.96, 0.94, and 0.91, respectively; without VT, the corresponding results were 0.70, 0.83, 0.88, 0.80, and 0.84. The differences in accuracy, sensitivity and NPV were significant between these approaches (p < 0.05). The AUCs with VT and without VT were 0.96 and 0.89, respectively (p < 0.05). Compared to the combined model and the radiomics signatures, The clinical model had a worse performance, and the differences were significant (p < 0.05). The combined model had the best performance, but the difference between the combined model and the radiomics signature weren't significant (p > 0.05). Conclusions: The CT-based radiomics signature could discriminate between WD and MD and PD HNSCC and might serve as a biomarker for preoperative grading.
Collapse
Affiliation(s)
- Wenli Wu
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Junyong Ye
- Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Chongqing, China
| | - Qi Wang
- Department of Information, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Jin Luo
- Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Chongqing, China
| | - Shengsheng Xu
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| |
Collapse
|
45
|
Quantification of Degree of Liver Fibrosis Using Fibrosis Area Fraction Based on Statistical Chi-Square Analysis of Heterogeneity of Liver Tissue Texture on Routine Ultrasound Images. Acad Radiol 2019; 26:1001-1007. [PMID: 30393055 DOI: 10.1016/j.acra.2018.10.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 10/12/2018] [Accepted: 10/12/2018] [Indexed: 12/17/2022]
Abstract
RATIONALE AND OBJECTIVES We present a novel method to quantify the degree of liver fibrosis using fibrosis area fraction based on statistical chi-square analysis of heterogeneity of echo texture within liver on routine ultrasound images. We demonstrate, in a clinical study, that fibrosis area fraction derived this way has the potential to become a noninvasive, quantitative radiometric discriminator of normal or low-grade liver fibrosis (Ishak fibrosis score range = F0-3) and advanced liver fibrosis or cirrhosis (Ishak fibrosis score range = F4-6) on routine ultrasound images. MATERIALS AND METHODS This retrospective patient study was institutional review board approved. Ultrasound images of 100 patients (61 males, 39 females; 18-81 years) who underwent nontargeted ultrasound-guided biopsy were randomly divided into two groups: a training group consisted of 31 cases, and a validation group that contained the rest cases. An investigator manually selected a primary region of interest (ROI; approximately 4-6 cm2) in the liver tissue while avoiding nonhepatic parenchyma. The primary ROI contained a large number of secondary ROIs (25 × 25 pixels) to maintain the precision of statistical analysis. Sample variance σ2 of image gradient (a texture feature related to the amount of edge structures) was calculated in secondary ROIs in a roster scan fashion. A theoretical derivation was presented to estimate population variance [Formula: see text] of image gradient across the primary ROI from mean gradient µ of secondary ROIs. The χ2 (χ2 = σ2/ [Formula: see text] ) was computed at each secondary ROI, forming a χ2 map of liver tissue heterogeneity. A cut-off value was optimized from datasets in the training group by comparing to the fibrosis grades determined by biopsy. This cut-off value was then applied to the datasets in the validation group to convert the χ2 maps into binary images, from which fibrosis area fractions (fraction of fibrosis area to the total area of the primary ROI) were calculated and entered in a statistical analysis. RESULTS In the training group, the optimal setting was found to be [Formula: see text] = 6.0, which resulted a maximum discrimination of F0-3 vs F4-6: p < 0.0001, area under curve = 0.985, sensitivity = 93.7%, specificity = 93.3%. When this setting was applied to the datasets in the validation group, a distinct separation was seen between the two classes (p < 0.0001). F0-3 class had an average fibrosis area fraction of 4.7% (1.7%-11.4%), whereas the F4-6 class had an average fibrosis area fraction of 17.3% (9.8%-29.6%). A strong correlation was demonstrated between the fibrosis area fraction and histological fibrosis grade determined by biopsy (area under curve = 0.89, sensitivity = 87.9%, specificity = 90.3%). CONCLUSION The presented method is a promising noninvasive tool for distinguishing normal or low-grade liver fibrosis (F0-3) and advanced liver fibrosis or cirrhosis (F4-6) from routine ultrasound images. These findings support the further development of texture heterogeneity analysis, particularly fibrosis area fraction, as a quantitative biomarker for distinguishing various liver fibrosis grades.
Collapse
|
46
|
Feeding the Data Monster: Data Science in Head and Neck Cancer for Personalized Therapy. J Am Coll Radiol 2019; 16:1695-1701. [PMID: 31238024 DOI: 10.1016/j.jacr.2019.05.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 05/18/2019] [Accepted: 05/28/2019] [Indexed: 01/14/2023]
Abstract
OBJECTIVE Head and neck carcinomas are clinically challenging malignancies because of tumor heterogeneities and resilient tumor subvolumes that require individualized treatment planning and delivery for an improved outcome. Although current approaches to diagnosis and therapy have boosted locoregional control, the long-term survival in this patient group remains unchanged over the last decades. A new approach to head and neck cancer management is therefore needed to better identify patient subgroups that are responsive to specific therapies. The aim of this article is to review the current status of knowledge and practice utilizing big data toward personalized therapy in head and neck cancers based on CT and PET imaging modalities. METHODS Literature published in English since 2000 was searched using Medline. Additional articles were retrieved via pearling of identified literature. Publications were reviewed and summarized in tabulated format. RESULTS Studies based on big data in head and neck cancer are limited; however, the field of radiomics is under continuous development and provides valuable input for personalized treatment. Using PET/PET CT biomarkers for patient treatment individualization and response prediction seems promising, especially in regard to detection of hypoxia and clonogenic cancer stem cells. Literature shows that macroscopic changes in medical images (whether structural or functional) are correlated with biologic and biochemical changes within a tumor. CONCLUSION Current trends in data science suggest that the ideal model for decision support in head and neck cancers should be based on human-machine collaboration, namely, on (1) software-based algorithms, (2) physician innovation collaboratives, and (3) clinician mix optimization.
Collapse
|
47
|
Mungai F, Verrone GB, Pietragalla M, Berti V, Addeo G, Desideri I, Bonasera L, Miele V. CT assessment of tumor heterogeneity and the potential for the prediction of human papillomavirus status in oropharyngeal squamous cell carcinoma. Radiol Med 2019; 124:804-811. [DOI: 10.1007/s11547-019-01028-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 03/11/2019] [Indexed: 01/10/2023]
|
48
|
Miller TA, Robinson KR, Li H, Seiwert TY, Haraf DJ, Lan L, Giger ML, Ginat DT. Prognostic value of pre-treatment CT texture analysis in combination with change in size of the primary tumor in response to induction chemotherapy for HPV-positive oropharyngeal squamous cell carcinoma. Quant Imaging Med Surg 2019; 9:399-408. [PMID: 31032187 DOI: 10.21037/qims.2019.03.08] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background To determine the additive value of quantitative radiomic texture features in predicting progression in human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) based on pre-treatment CT. Methods Retrospective analysis of a single-center cohort of adult patients enrolled in a response-adapted radiation volume de-escalation trial treated with induction chemotherapy. Texture analysis of HPV-positive OPSCC was performed via primary tumor site contouring on pre-treatment contrast-enhanced CT scans. Percent change in size of the tumor in response to induction chemotherapy based on RECIST 1.1 criteria and progression free survival were clinically determined for this cohort. Receiver operating characteristic (ROC) analysis was performed to compare the accuracy of percent change in tumor size after induction chemotherapy with a combination of change in tumor size and radiomic texture features for predicting tumor progression. Results Radiomic texture analysis of the primary tumors in 38 patients with OPSCC depicted on pre-treatment neck CT scans using skewness and entropy in combination with percent change in tumor size after induction chemotherapy yielded a statistically significant increase in accuracy for predicting tumor progression over change in tumor size alone, with an area under the curve of 0.80 versus 0.56 (one-tailed P=0.0087). Conclusions This pilot study suggests that disease progression in patients with HPV-positive OPSCC is more accurately predicted using a combination of texture features on pre-treatment CT scans, along with change in tumor size compared to change in tumor size alone and could therefore serve as a radiomic texture signature.
Collapse
Affiliation(s)
- Tamari A Miller
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Kayla R Robinson
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Hui Li
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Tanguy Y Seiwert
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Daniel J Haraf
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Li Lan
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Maryellen L Giger
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Daniel T Ginat
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| |
Collapse
|
49
|
Kuno H, Garg N, Qureshi MM, Chapman MN, Li B, Meibom SK, Truong MT, Takumi K, Sakai O. CT Texture Analysis of Cervical Lymph Nodes on Contrast-Enhanced [ 18F] FDG-PET/CT Images to Differentiate Nodal Metastases from Reactive Lymphadenopathy in HIV-Positive Patients with Head and Neck Squamous Cell Carcinoma. AJNR Am J Neuroradiol 2019; 40:543-550. [PMID: 30792253 DOI: 10.3174/ajnr.a5974] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 01/05/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND AND PURPOSE Differentiating nodal metastases from reactive adenopathy in HIV-infected patients with [18F] FDG-PET/CT can be challenging because lymph nodes in HIV-positive patients often show increased [18F] FDG uptake. The purpose of this study was to assess CT textural analysis characteristics of HIV-positive and HIV-negative lymph nodes on [18F] FDG-PET/CT to differentiate nodal metastases from disease-specific nodal reactivity. MATERIALS AND METHODS Nine HIV-positive patients with head and neck squamous cell carcinoma (7 men, 2 women; 29-62 years of age; median age, 48 years) with 22 lymph nodes (≥1 cm) who underwent contrast-enhanced CT with [18F] FDG-PET followed by pathologic evaluation of cervical lymph nodes were retrospectively reviewed. Twenty-six HIV-negative patients with head and neck squamous cell carcinoma with 61 lymph nodes were evaluated as a control group. Each lymph node was manually segmented, and an in-house-developed Matlab-based texture analysis program extracted 41 texture features from each segmented volume. A mixed linear regression model was used to compare the pathologically proved malignant lymph nodes with benign nodes in the 2 enrolled groups. RESULTS Thirteen (59%) lymph nodes in the HIV-positive group and 22 (36%) lymph nodes in the HIV-negative control group were confirmed as positive for metastases. There were 7 histogram features (P = .017-0.032), 3 gray-level co-occurrence features (P = .009-.025), and 9 gray-level run-length features (P < .001-.033) that demonstrated a significant difference in HIV-positive patients with either benign or malignant lymph nodes. CONCLUSIONS CT texture analysis may be useful as a noninvasive method of obtaining additional quantitative information to differentiate nodal metastases from disease-specific nodal reactivity in HIV-positive patients with head and neck squamous cell carcinoma.
Collapse
Affiliation(s)
- H Kuno
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Department of Diagnostic Radiology (H.K.), National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - N Garg
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - M M Qureshi
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Radiation Oncology (M.M.Q., M.T.T., O.S.)
| | - M N Chapman
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - B Li
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - S K Meibom
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - M T Truong
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Radiation Oncology (M.M.Q., M.T.T., O.S.)
| | - K Takumi
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Department of Radiology (K.T.), Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - O Sakai
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.) .,Radiation Oncology (M.M.Q., M.T.T., O.S.).,Otolaryngology-Head and Neck Surgery (O.S.), Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
| |
Collapse
|
50
|
Gohel A, Oda M, Katkar AS, Sakai O. Multidetector Row Computed Tomography in Maxillofacial Imaging. Dent Clin North Am 2019; 62:453-465. [PMID: 29903561 DOI: 10.1016/j.cden.2018.03.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Multidetector row CT (MDCT) offers superior soft tissue characterization and is useful for diagnosis of odontogenic and nonodontogenic cysts and tumors, fibro-osseous lesions, inflammatory, malignancy, metastatic lesions, developmental abnormalities, and maxillofacial trauma. The rapid advances in MDCT technology, including perfusion CT, dual-energy CT, and texture analysis, will be an integrated anatomic and functional high-resolution scan, which will help in diagnosis of maxillofacial lesions and overall patient care.
Collapse
Affiliation(s)
- Anita Gohel
- Oral and Maxillofacial Pathology and Radiology, College of Dentistry, The Ohio State University, 3165 Postle Hall, 305 West 12th Avenue, Columbus, OH 43210-1267, USA.
| | - Masafumi Oda
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Avenue, Boston, MA 02118, USA; Division of Oral and Maxillofacial Radiology, Kyushu Dental University, 2-6-1 Manazuru, Kokurakita-ku, Kitakyushu 803-8580, Japan
| | - Amol S Katkar
- Department of Radiology, Brook Army Medical Center, 3851 Roger Brooke Drive, Fort Sam Houston, TX 78234-6200, USA
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Avenue, Boston, MA 02118, USA; Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Avenue, Boston, MA 02118, USA; Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, 820 Harrison Avenue, Boston, MA 02118, USA
| |
Collapse
|