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Tran K, Ginzburg D, Hong W, Attenberger U, Ko HS. Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks. Eur Radiol 2024:10.1007/s00330-024-10736-1. [PMID: 38625613 DOI: 10.1007/s00330-024-10736-1] [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: 11/13/2023] [Revised: 02/07/2024] [Accepted: 03/04/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Lung cancer, the second most common cancer, presents persistently dismal prognoses. Radiomics, a promising field, aims to provide novel imaging biomarkers to improve outcomes. However, clinical translation faces reproducibility challenges, despite efforts to address them with quality scoring tools. OBJECTIVE This study had two objectives: 1) identify radiomics biomarkers in post-radiotherapy stage III/IV nonsmall cell lung cancer (NSCLC) patients, 2) evaluate research quality using the CLEAR (CheckList_for_EvaluAtion_of_Radiomics_research), RQS (Radiomics_Quality_Score) frameworks, and formulate an amalgamated CLEAR-RQS tool to enhance scientific rigor. MATERIALS AND METHODS A systematic literature review (Jun-Aug 2023, MEDLINE/PubMed/SCOPUS) was conducted concerning stage III/IV NSCLC, radiotherapy, and radiomic features (RF). Extracted data included study design particulars, such as sample size, radiotherapy/CT technique, selected RFs, and endpoints. CLEAR and RQS were merged into a CLEAR-RQS checklist. Three readers appraised articles utilizing CLEAR, RQS, and CLEAR-RQS metrics. RESULTS Out of 871 articles, 11 met the inclusion/exclusion criteria. The Median cohort size was 91 (range: 10-337) with 9 studies being single-center. No common RF were identified. The merged CLEAR-RQS checklist comprised 61 items. Most unreported items were within CLEAR's "methods" and "open-source," and within RQS's "phantom-calibration," "registry-enrolled prospective-trial-design," and "cost-effective-analysis" sections. No study scored above 50% on RQS. Median CLEAR scores were 55.74% (32.33/58 points), and for RQS, 17.59% (6.3/36 points). CLEAR-RQS article ranking fell between CLEAR and RQS and aligned with CLEAR. CONCLUSION Radiomics research in post-radiotherapy stage III/IV NSCLC exhibits variability and frequently low-quality reporting. The formulated CLEAR-RQS checklist may facilitate education and holds promise for enhancing radiomics research quality. CLINICAL RELEVANCE STATEMENT Current radiomics research in the field of stage III/IV postradiotherapy NSCLC is heterogenous, lacking reproducibility, with no identified imaging biomarker. Radiomics research quality assessment tools may enhance scientific rigor and thereby facilitate radiomics translation into clinical practice. KEY POINTS There is heterogenous and low radiomics research quality in postradiotherapy stage III/IV nonsmall cell lung cancer. Barriers to reproducibility are small cohort size, nonvalidated studies, missing technical parameters, and lack of data, code, and model sharing. CLEAR (CheckList_for_EvaluAtion_of_Radiomics_research), RQS (Radiomics_Quality_Score), and the amalgamated CLEAR-RQS tool are useful frameworks for assessing radiomics research quality and may provide a valuable resource for educational purposes in the field of radiomics.
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Affiliation(s)
- Kevin Tran
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia
- Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Parkville, VIC 3052, Australia
| | - Daniel Ginzburg
- Department of Diagnostic and Interventional Radiology, Venusberg Campus 1, 53127, Bonn, Germany
| | - Wei Hong
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia
| | - Ulrike Attenberger
- Department of Diagnostic and Interventional Radiology, Venusberg Campus 1, 53127, Bonn, Germany
| | - Hyun Soo Ko
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia.
- Department of Diagnostic and Interventional Radiology, Venusberg Campus 1, 53127, Bonn, Germany.
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, 305 Grattan St, Melbourne, VIC 3000, Australia.
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Ding Z, Zhang C, Yao Q, Liu Q, Lv L, Shi S. Delta Radiomics Model for the Prediction of Overall Survival and Local Recurrence in Small Cell Lung Cancer Patients After Chemotherapy. Acad Radiol 2024; 31:1168-1179. [PMID: 37932167 DOI: 10.1016/j.acra.2023.10.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 11/08/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate the validity of CT-based delta radiomics signatures in predicting overall survival (OS) and local recurrence (LR) in small cell lung cancer (SCLC) patients after chemotherapy. MATERIALS AND METHODS Retrospectively enrolled 136 SCLC patients were split into training and testing cohorts. Radiomics features were extracted from CT images before, after the second, and the fourth cycle of chemotherapy. Delta radiomics features were obtained by calculating the net changes of features. Three radiomics signatures (R1, R2, and R3) and three delta radiomics signatures (R21, R31, and R32) were developed. The best signature was defined as the radiomics risk signature (RRS). The significant clinicoradiological factors and RRS of OS or LR were applied to build the combined model. RRS was also investigated in the subgroups based on stage and treatment regimens, respectively. RESULTS Delta radiomics models presented improved performance. R32 signature demonstrated the highest C-indices in the training and testing cohorts, with C-indices of 0.850 and 0.834 in the OS arm, and 0.723 and 0.737 in the LR arm, respectively. The incremental performance was observed after the clinicoradiological characteristics integrated into the RRSOS, with C-indexes of 0.857 and 0.836, respectively. Furthermore, the stratified analysis also confirmed the ability of RRS based on the stage and treatment regimen subgroups in the OS and LR arms, respectively. CONCLUSION Delta radiomics signatures could improve the personalized prediction of OS and LR at the early stage of chemotherapy in SCLC patients. R32 signature performed the highest performance.
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Affiliation(s)
- Zhimin Ding
- Department of Radiology, the First Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China
| | - Chengmeng Zhang
- Department of Radiology, the First Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China
| | - Qi Yao
- Department of Radiology, the First Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China
| | - Qifeng Liu
- Department of Radiology, the First Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China
| | - Lei Lv
- Department of Radiology, the First Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China
| | - Suhua Shi
- Clinical Institute, the First Affiliated Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241000, China.
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Yang P, Shan J, Ge X, Zhou Q, Ding M, Niu T, Du J. Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study. Phys Eng Sci Med 2024; 47:295-307. [PMID: 38165634 DOI: 10.1007/s13246-023-01366-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 12/06/2023] [Indexed: 01/04/2024]
Abstract
This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features. The CBCT features which are reproducible and interchangeable with pCT are selected for modeling analysis. The delta features between fractions are calculated to depict tumor change. The patients with 4-fraction SBRT are only used for screening robust features. In patients receiving 5-fraction SBRT, the predictive ability of both original and delta CBCT features for two-level treatment response (local efficacy vs. local non-efficacy; complete response (CR) vs. partial response (PR)) is assessed by utilizing multivariable logistic regression with leave-one-out cross-validation. Additionally, univariate analysis is conducted to validate the capability of CBCT features in identifying local efficacy in patients receiving 3-fraction SBRT. In patients receiving 5-fraction SBRT, the combined models incorporating original and delta CBCT radiomics features demonstrate higher area under the curve (AUC) values compared to models using either original or delta features alone for both classification tasks. The AUC values for predicting local efficacy vs. local non-efficacy are 0.58 for original features, 0.82 for delta features, and 0.90 for combined features. For distinguishing PR from CR, the respective AUC values for original, delta and combined features are 0.79, 0.80, and 0.89. In patients receiving 3-fraction SBRT, eight valuable CBCT radiomics features are identified for predicting local efficacy. The combination of original and delta radiomics derived from fractionated CBCT images in liver cancer patients undergoing SBRT shows promise in providing comprehensive information for predicting treatment response.
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Affiliation(s)
- Pengfei Yang
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Jingjing Shan
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xin Ge
- School of Science, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Qinxuan Zhou
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mingchao Ding
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China
| | - Tianye Niu
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.
| | - Jichen Du
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
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Choi W, Jia Y, Kwak J, Werner-Wasik M, Dicker AP, Simone NL, Storozynsky E, Jain V, Vinogradskiy Y. Novel Functional Radiomics for Prediction of Cardiac Positron Emission Tomography Avidity in Lung Cancer Radiotherapy. JCO Clin Cancer Inform 2024; 8:e2300241. [PMID: 38452302 PMCID: PMC10939651 DOI: 10.1200/cci.23.00241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/22/2023] [Accepted: 01/26/2024] [Indexed: 03/09/2024] Open
Abstract
PURPOSE Traditional methods of evaluating cardiotoxicity focus on radiation doses to the heart. Functional imaging has the potential to provide improved prediction for cardiotoxicity for patients with lung cancer. Fluorine-18 (18F) fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging is routinely obtained in a standard cancer staging workup. This work aimed to develop a radiomics model predicting clinical cardiac assessment using 18F-FDG PET/CT scans before thoracic radiation therapy. METHODS Pretreatment 18F-FDG PET/CT scans from three study populations (N = 100, N = 39, N = 70) were used, comprising two single-institutional protocols and one publicly available data set. A clinician (V.J.) classified the PET/CT scans per clinical cardiac guidelines as no uptake, diffuse uptake, or focal uptake. The heart was delineated, and 210 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. Training data were divided into training (80%)/validation (20%) sets. Feature reduction was performed using the Wilcoxon test, hierarchical clustering, and recursive feature elimination. Ten-fold cross-validation was carried out for training, and the accuracy of the models to predict clinical cardiac assessment was reported. RESULTS From 202 of 209 scans, cardiac FDG uptake was scored as no uptake (39.6%), diffuse uptake (25.3%), and focal uptake (35.1%), respectively. Sixty-two independent radiomics features were reduced to nine clinically pertinent features. The best model showed 93% predictive accuracy in the training data set and 80% and 92% predictive accuracy in two external validation data sets. CONCLUSION This work used an extensive patient data set to develop a functional cardiac radiomic model from standard-of-care 18F-FDG PET/CT scans, showing good predictive accuracy. The radiomics model has the potential to provide an automated method to predict existing cardiac conditions and provide an early functional biomarker to identify patients at risk of developing cardiac complications after radiotherapy.
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Affiliation(s)
- Wookjin Choi
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Yingcui Jia
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Jennifer Kwak
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO
| | - Maria Werner-Wasik
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Adam P. Dicker
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Nicole L. Simone
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Eugene Storozynsky
- Department of Cardiology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Varsha Jain
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
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Fiste O, Gkiozos I, Charpidou A, Syrigos NK. Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC. Cancers (Basel) 2024; 16:831. [PMID: 38398222 PMCID: PMC10887017 DOI: 10.3390/cancers16040831] [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: 01/31/2024] [Revised: 02/12/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.
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Affiliation(s)
- Oraianthi Fiste
- Oncology Unit, Third Department of Internal Medicine and Laboratory, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.G.); (A.C.); (N.K.S.)
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Nakamoto T, Yamashita H, Jinnouchi H, Nawa K, Imae T, Takenaka S, Aoki A, Ohta T, Ozaki S, Nozawa Y, Nakagawa K. Cone-beam computed-tomography-based delta-radiomic analysis for investigating prognostic power for esophageal squamous cell cancer patients undergoing concurrent chemoradiotherapy. Phys Med 2024; 117:103182. [PMID: 38086310 DOI: 10.1016/j.ejmp.2023.103182] [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: 07/21/2023] [Revised: 10/22/2023] [Accepted: 11/19/2023] [Indexed: 01/15/2024] Open
Abstract
PURPOSE To investigate the prognostic power of cone-beam computed-tomography (CBCT)-based delta-radiomics in esophageal squamous cell cancer (ESCC) patients treated with concurrent chemoradiotherapy (CCRT). METHODS We collected data from 26 ESCC patients treated with CCRT. CBCT images acquired at five time points (1st-5th week) per patient during CCRT were used in this study. Radiomic features were extracted from the five CBCT images on the gross tumor volumes. Then, 17 delta-radiomic feature sets derived from five types of calculations were obtained for all the cases. Leave-one-out cross-validation was applied to investigate the prognostic power of CBCT-based delta-radiomic features. Feature selection and construction of a prediction model using Coxnet were performed using training samples. Then, the test sample was classified into high or low risk in each cross-validation fold. Survival analysis for the two groups were performed to evaluate the prognostic power of the extracted CBCT-based delta-radiomic features. RESULTS Four delta-radiomic feature sets indicated significant differences between the high- and low-risk groups (p < 0.05). The highest C-index in the 17 delta-radiomic feature sets was 0.821 (95 % confidence interval, 0.735-0.907). That feature set had p-value of the log-rank test and hazard ratio of 0.003 and 4.940 (95 % confidence interval, 1.391-17.544), respectively. CONCLUSIONS We investigated the potential of using CBCT-based delta-radiomics for prognosis of ESCC patients treated with CCRT. It was demonstrated that delta-radiomic feature sets based on the absolute value of relative difference obtained from the early to the middle treatment stages have high prognostic power for ESCC.
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Affiliation(s)
- Takahiro Nakamoto
- Department of Biological Science and Engineering, Faculty of Health Sciences, Hokkaido University, N12-W5, Kita-ku, Sapporo, Hokkaido 060-0812, Japan; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Hideomi Yamashita
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Haruka Jinnouchi
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Kanabu Nawa
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Toshikazu Imae
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Shigeharu Takenaka
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Atsushi Aoki
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Takeshi Ohta
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Sho Ozaki
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; Graduate School of Science and Technology, Hirosaki University, 3 Bunkyo, Hirosaki, Aomori 036-8561, Japan
| | - Yuki Nozawa
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Keiichi Nakagawa
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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Boldrini L, D'Aviero A, De Felice F, Desideri I, Grassi R, Greco C, Iorio GC, Nardone V, Piras A, Salvestrini V. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). LA RADIOLOGIA MEDICA 2024; 129:133-151. [PMID: 37740838 DOI: 10.1007/s11547-023-01708-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/16/2023] [Indexed: 09/25/2023]
Abstract
INTRODUCTION The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.
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Affiliation(s)
- Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS "A. Gemelli", Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea D'Aviero
- Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy
| | - Francesca De Felice
- Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy
- Oncological and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Carlo Greco
- Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | | | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy.
| | - Viola Salvestrini
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy
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Abe K, Kadoya N, Ito K, Tanaka S, Nakajima Y, Hashimoto S, Suda Y, Uno T, Jingu K. Evaluation of the MVCT-based radiomic features as prognostic factor in patients with head and neck squamous cell carcinoma. BMC Med Imaging 2023; 23:102. [PMID: 37528392 PMCID: PMC10391970 DOI: 10.1186/s12880-023-01055-w] [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: 12/23/2022] [Accepted: 07/18/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Megavoltage computed tomography (MVCT) images acquired during each radiotherapy session may be useful for delta radiomics. However, no studies have examined whether the MVCT-based radiomics has prognostic power. Therefore, the purpose of this study was to examine the prognostic power of the MVCT-based radiomics for head and neck squamous cell carcinoma (HNSCC) patients. METHODS 100 HNSCC patients who received definitive radiotherapy were analyzed and divided into two groups: training (n = 70) and test (n = 30) sets. MVCT images obtained using TomoTherapy for the first fraction of radiotherapy and planning kilovoltage CT (kVCT) images obtained using Aquilion LB CT scanner were analyzed. Primary gross tumor volume (GTV) was propagated from kVCT to MVCT images using rigid registration, and 107 radiomic features were extracted from the GTV in MVCT and kVCT images. Least absolute shrinkage and selection operator (LASSO) Cox regression model was used to examine the association between overall survival (OS) and rad score calculated for each patient by weighting the feature value through the coefficient when features were selected. Then, the predictive values of MVCT-based and kVCT-based rad score and patient-, treatment-, and tumor-specific factors were evaluated. RESULTS C-indices of the rad score for MVCT- and kVCT-based radiomics were 0.667 and 0.685, respectively. The C-indices of 6 clinical factors were 0.538-0.622. The 3-year OS was significantly different between high- and low-risk groups according to the MVCT-based rad score (50% vs. 83%; p < 0.01). CONCLUSIONS Our results suggested that MVCT-based radiomics had stronger prognostic power than any single clinical factor and was a useful prognostic factor when predicting OS in HNSCC patients.
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Affiliation(s)
- Kota Abe
- Department of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
- Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, 113-8677, Japan
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo- machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo- machi, Aoba-ku, Sendai, 980-8574, Japan.
| | - Kei Ito
- Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, 113-8677, Japan
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo- machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Yujiro Nakajima
- Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, 113-8677, Japan
- Department of Radiological Sciences, Komazawa University, 1-23-1 komazawa, Setagaya-ku, Tokyo, 154-8525, Japan
| | - Shimpei Hashimoto
- Saitama Prefectural Cancer Center, 780 large section of a town Omuro, Ina-machi, Kitaadachi- gun, Saitama, 362-0806, Japan
| | - Yuhi Suda
- Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, 113-8677, Japan
| | - Takashi Uno
- Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo- machi, Aoba-ku, Sendai, 980-8574, Japan
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Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol 2023; 93:97-113. [PMID: 37211292 DOI: 10.1016/j.semcancer.2023.05.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
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Affiliation(s)
- Mitchell Chen
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Susan J Copley
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Patrizia Viola
- North West London Pathology, Charing Cross Hospital, Fulham Palace Rd, London W6 8RF, UK
| | - Haonan Lu
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK.
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Paudyal R, Shah AD, Akin O, Do RKG, Konar AS, Hatzoglou V, Mahmood U, Lee N, Wong RJ, Banerjee S, Shin J, Veeraraghavan H, Shukla-Dave A. Artificial Intelligence in CT and MR Imaging for Oncological Applications. Cancers (Basel) 2023; 15:cancers15092573. [PMID: 37174039 PMCID: PMC10177423 DOI: 10.3390/cancers15092573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Akash D Shah
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Usman Mahmood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Richard J Wong
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | | | | | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
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Brown KH, Payan N, Osman S, Ghita M, Walls GM, Patallo IS, Schettino G, Prise KM, McGarry CK, Butterworth KT. Development and optimisation of a preclinical cone beam computed tomography-based radiomics workflow for radiation oncology research. Phys Imaging Radiat Oncol 2023; 26:100446. [PMID: 37252250 PMCID: PMC10213103 DOI: 10.1016/j.phro.2023.100446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/03/2023] [Accepted: 05/08/2023] [Indexed: 05/31/2023] Open
Abstract
Background and purpose Radiomics features derived from medical images have the potential to act as imaging biomarkers to improve diagnosis and predict treatment response in oncology. However, the complex relationships between radiomics features and the biological characteristics of tumours are yet to be fully determined. In this study, we developed a preclinical cone beam computed tomography (CBCT) radiomics workflow with the aim to use in vivo models to further develop radiomics signatures. Materials and methods CBCT scans of a mouse phantom were acquired using onboard imaging from a small animal radiotherapy research platform (SARRP, Xstrahl). The repeatability and reproducibility of radiomics outputs were compared across different imaging protocols, segmentation sizes, pre-processing parameters and materials. Robust features were identified and used to compare scans of two xenograft mouse tumour models (A549 and H460). Results Changes to the radiomics workflow significantly impact feature robustness. Preclinical CBCT radiomics analysis is feasible with 119 stable features identified from scans imaged at 60 kV, 25 bin width and 0.26 mm slice thickness. Large variation in segmentation volumes reduced the number of reliable radiomics features for analysis. Standardization in imaging and analysis parameters is essential in preclinical radiomics analysis to improve accuracy of outputs, leading to more consistent and reproducible findings. Conclusions We present the first optimised workflow for preclinical CBCT radiomics to identify imaging biomarkers. Preclinical radiomics has the potential to maximise the quantity of data captured in in vivo experiments and could provide key information supporting the wider application of radiomics.
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Affiliation(s)
- Kathryn H. Brown
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Neree Payan
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Sarah Osman
- University College London Hospitals NHS Foundation Trust Department of Radiotherapy, London, UK
| | - Mihaela Ghita
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Gerard M. Walls
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
- Cancer Centre, Belfast Health & Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK
| | | | | | - Kevin M. Prise
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Conor K. McGarry
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
- Cancer Centre, Belfast Health & Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK
| | - Karl T. Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
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Luximon DC, Neylon J, Lamb JM. Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy. Phys Imaging Radiat Oncol 2023; 25:100427. [PMID: 36937493 PMCID: PMC10020677 DOI: 10.1016/j.phro.2023.100427] [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: 12/12/2022] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
Background and purpose Currently, there is no robust indicator within the Cone-Beam Computed Tomography (CBCT) DICOM headers as to which anatomical region is present on the scan. This can be a predicament to CBCT-based algorithms trained on specific body regions, such as auto-segmentation and radiomics tools used in the radiotherapy workflow. We propose an anatomical region labeling (ARL) algorithm to classify CBCT scans into four distinct regions: head & neck, thoracic-abdominal, pelvis, and extremity. Materials and methods Algorithm training and testing was performed on 3,802 CBCT scans from 596 patients treated at our radiotherapy center. The ARL model, which consists of a convolutional neural network, makes use of a single CBCT coronal slice to output a probability of occurrence for each of the four classes. ARL was evaluated on the test dataset composed of 1,090 scans and compared to a support vector machine (SVM) model. ARL was also used to label CBCT treatment scans for 22 consecutive days as part of a proof-of-concept implementation. A validation study was performed on the first 100 unique patient scans to evaluate the functionality of the tool in the clinical setting. Results ARL achieved an overall accuracy of 99.2% on the test dataset, outperforming the SVM (91.5% accuracy). Our validation study has shown strong agreement between the human annotations and ARL predictions, with accuracies of 99.0% for all four regions. Conclusion The high classification accuracy demonstrated by ARL suggests that it may be employed as a pre-processing step for site-specific, CBCT-based radiotherapy tools.
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Affiliation(s)
- Dishane C Luximon
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - John Neylon
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - James M Lamb
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
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Zhang C, Heng X, Neng W, Chen H, Sun A, Li J, Wang M. Prediction of high infiltration levels in pituitary adenoma using MRI-based radiomics and machine learning. Chin Neurosurg J 2022; 8:21. [PMID: 35962442 PMCID: PMC9373412 DOI: 10.1186/s41016-022-00290-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/13/2022] [Indexed: 12/03/2022] Open
Abstract
Background Infiltration is important for the surgical planning and prognosis of pituitary adenomas. Differences in preoperative diagnosis have been noted. The aim of this article is to assess the accuracy of machine learning analysis of texture-derived parameters of pituitary adenoma obtained from preoperative MRI for the prediction of high infiltration. Methods A total of 196 pituitary adenoma patients (training set: n = 176; validation set: n = 20) were enrolled in this retrospective study. In total, 4120 quantitative imaging features were extracted from CE-T1 MR images. To select the most informative features, the least absolute shrinkage and selection operator (LASSO) and variance threshold method were performed. The linear support vector machine (SVM) was used to fit the predictive model based on infiltration features. Furthermore, the receiver operating characteristic curve (ROC) was generated, and the diagnostic performance of the model was evaluated by calculating the area under the curve (AUC), accuracy, precision, recall, and F1 value. Results A variance threshold of 0.85 was used to exclude 16 features with small differences using the LASSO algorithm, and 19 optimal features were finally selected. The SVM models for predicting high infiltration yielded an AUC of 0.86 (sensitivity: 0.81, specificity 0.79) in the training set and 0.73 (sensitivity: 0.87, specificity: 0.80) in the validation set. The four evaluation indicators of the predictive model achieved good diagnostic capabilities in the training set (accuracy: 0.80, precision: 0.82, recall: 0.81, F1 score: 0.81) and independent verification set (accuracy: 0.85, precision: 0.93, recall: 0.87, F1 score: 0.90). Conclusions The radiomics model developed in this study demonstrates efficacy for the prediction of pituitary adenoma infiltration. This model could potentially aid neurosurgeons in the preoperative prediction of infiltration in PAs and contribute to the selection of ideal surgical strategies. Supplementary Information The online version contains supplementary material available at 10.1186/s41016-022-00290-4.
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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Robustness and reproducibility of radiomics in T2 weighted images from magnetic resonance image guided linear accelerator in a phantom study. Phys Med 2022; 96:130-139. [PMID: 35287100 DOI: 10.1016/j.ejmp.2022.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 02/07/2022] [Accepted: 03/04/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Quantitative radiomics features extracted from medical images have been shown to provide value in predicting clinical outcomes. The study for robustness and reproducibility of radiomics features obtained with magnetic resonance image guided linear accelerator (MR-Linac) is insufficient. The objective of this work was to investigate the stability of radiomics features extracted from T2-weighted images of MR-Linac for five common effect factors. MATERIALS AND METHOD In this work, ten jellies, five fruits/vegetables, and a dynamic phantom were used to evaluate the impact of test-retest, intraobserver, varied thicknesses, radiation, and motion. These phantoms were scanned on a 1.5 T MRI system of MR-Linac. For test-retest data, the phantoms were scanned twice with repositioning within 15 min. To assess for intraobserver comparison, the segmentation of MR images was repeated by one observer in a double-blind manner. Three slice thicknesses (1.2 mm, 2.4 mm, and 4.8 mm) were used to select robust features that were insensitive to different thicknesses. The effect of radiation on features was studied by acquiring images when the beam was on. Common movement images of patients during radiotherapy were simulated by a dynamic phantom with five motion states to study the motion effect. A total of 1409 radiomics features, including shape features, first-order features, and texture features, were extracted from the original, wavelet, square, logarithmic, exponential and gradient images. The robustness and reproducibility features were evaluated using the concordance correlation coefficient (CCC). RESULT The intraobserver group had the most robust features (936/1079, 86.7%), while the group of motion effects had the lowest robustness (56/936, 6.0%), followed by the group of different thickness cohorts (374/936, 40.0%). The stability of features in the test-retest and radiation groups was 1072 of 1312 (81.7%) and 810 of 936 (86.5%), respectively. Overall, 25 of 1409 (2.4%) radiomics features remained robust in all five tests, mostly focusing on the image type of the wavelet. The number of stable features extracted from when the beam was on was less than that extracted when the beam was off. Shape features were the most robust of all of the features in all of the groups, excluding the motion group. CONCLUSION Compared with other factors fewer features remained robust to the effect of motion. This result emphasizes the need to consider the effect of respiration motion. The study for T2-weighted images from MR-Linac under different conditions will help us to build a robust predictive model applicable for radiotherapy.
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Duan W, Xiong B, Tian T, Zou X, He Z, Zhang L. Radiomics in Nasopharyngeal Carcinoma. CLINICAL MEDICINE INSIGHTS: ONCOLOGY 2022; 16:11795549221079186. [PMID: 35237090 PMCID: PMC8883403 DOI: 10.1177/11795549221079186] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 01/20/2022] [Indexed: 12/24/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies, and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy alone, concurrent chemoradiotherapy, and induction chemotherapy combined with concurrent chemoradiotherapy can be used according to different grades. Treatment options and prognoses vary greatly depending on the grade of disease in the patients. Accurate grading and risk assessment are required. Recently, radiomics has combined a large amount of invisible high-dimensional information extracted from computed tomography, magnetic resonance imaging, or positron emission tomography with powerful computing capabilities of machine-learning algorithms, providing the possibility to achieve an accurate diagnosis and individualized treatment for cancer patients. As an effective tumor biomarker of NPC, the radiomic signature has been widely used in grading, differential diagnosis, prediction of prognosis, evaluation of treatment response, and early identification of therapeutic complications. The process of radiomic research includes image segmentation, feature extraction, feature selection, model establishment, and evaluation. Many open-source or commercial tools can be used to achieve these procedures. The development of machine-learning algorithms provides more possibilities for radiomics research. This review aimed to summarize the application of radiomics in NPC and introduce the basic process of radiomics research.
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Affiliation(s)
- Wenyue Duan
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Bingdi Xiong
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Ting Tian
- Department of Clinical Medicine, Chengdu Medical College, Chengdu, People's Republic of China
| | - Xinyun Zou
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Zhennan He
- Department of Clinical Medicine, Chengdu Medical College, Chengdu, People's Republic of China
| | - Ling Zhang
- Department of Oncology, People's Liberation Army The General Hospital of Western Theater Command, Chengdu, People's Republic of China
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Arimura H, Kodama T, Urakami A, Kamezawa H, Hirose TA, Ninomiya K. [6. Imaging Biopsy for Assisting Cancer Precision Therapy -Information Extracted from Radiomics]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:219-224. [PMID: 35185102 DOI: 10.6009/jjrt.780213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Hidetaka Arimura
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University
| | - Takumi Kodama
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
| | - Akimasa Urakami
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
| | - Hidemi Kamezawa
- Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University
| | - Taka-Aki Hirose
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital
| | - Kenta Ninomiya
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University
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Sushentsev N, Rundo L, Blyuss O, Nazarenko T, Suvorov A, Gnanapragasam VJ, Sala E, Barrett T. Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance. Eur Radiol 2022; 32:680-689. [PMID: 34255161 PMCID: PMC8660717 DOI: 10.1007/s00330-021-08151-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/27/2021] [Accepted: 06/13/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). METHODS The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test. RESULTS The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77). CONCLUSIONS PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. KEY POINTS • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK.
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| | - Leonardo Rundo
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Oleg Blyuss
- School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield, UK
- Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Applied Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Tatiana Nazarenko
- Department of Mathematics and Institute for Women's Health, University College London, London, UK
| | - Aleksandr Suvorov
- World-Class Research Center "Digital Biodesign and Personalised Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Vincent J Gnanapragasam
- Division of Urology, Department of Surgery, University of Cambridge, Cambridge, UK
- Cambridge Urology Translational Research and Clinical Trials Office, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
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Sellami S, Bourbonne V, Hatt M, Tixier F, Bouzid D, Lucia F, Pradier O, Goasduff G, Visvikis D, Schick U. Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images. Acta Oncol 2022; 61:73-80. [PMID: 34632924 DOI: 10.1080/0284186x.2021.1983207] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. MATERIAL AND METHODS A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. RESULTS Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p < .001), and 0.99 (p < .001) for clinical, CBCT4-radiomics (Coarseness) and clinical + radiomics based models respectively. The mean AUC of this last model on a 5-fold cross-validation was 0.80 (±0.09). On the testing cohort, the best prediction was given by the combined model (balanced accuracy [BAcc] 0.67 , p < .001). CONCLUSIONS We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models.
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Affiliation(s)
- S. Sellami
- Radiation Oncology Department, University Hospital, Brest, France
| | - V. Bourbonne
- Radiation Oncology Department, University Hospital, Brest, France
| | - M. Hatt
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - F. Tixier
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - D. Bouzid
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - F. Lucia
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - O. Pradier
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - G. Goasduff
- Radiation Oncology Department, University Hospital, Brest, France
| | - D. Visvikis
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - U. Schick
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
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Wang H, Zhou Y, Wang X, Zhang Y, Ma C, Liu B, Kong Q, Yue N, Xu Z, Nie K. Reproducibility and Repeatability of CBCT-Derived Radiomics Features. Front Oncol 2021; 11:773512. [PMID: 34869015 PMCID: PMC8637922 DOI: 10.3389/fonc.2021.773512] [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: 09/10/2021] [Accepted: 10/27/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose This study was conducted in order to determine the reproducibility and repeatability of cone-beam computed tomography (CBCT) radiomics features. Methods The first-, second-, and fifth-day CBCT images from 10 head and neck (H&N) cancer patients and 10 pelvic cancer patients were retrospectively collected for this study. Eighteen common radiomics features were extracted from the longitudinal CBCT images using two radiomics packages. The reproducibility of CBCT-derived radiomics features was assessed using the first-day image as input and compared across the two software packages. The site-specific intraclass correlation coefficient (ICC) was used to quantitatively assess the agreement between packages. The repeatability of CBCT-based radiomics features was evaluated by comparing the following days of CBCT to the first-day image and quantified using site-specific concordance correlation coefficient (CCC). Furthermore, the correlation with volume for all the features was assessed with linear regression and R2 as correlation parameters. Results The first-order histogram-based features such as skewness and entropy showed good agreement computed in either software package (ICCs ≥ 0.80), while the kurtosis measurements were consistent in H&N patients between the two software tools but not in pelvic cases. The ICCs for GLCM-based features showed good agreement (ICCs ≥ 0.80) between packages in both H&N and pelvic groups except for the GLCM-correction. The GLRLM-based texture features were overall less consistent as calculated by the two different software packages compared with the GLCM-based features. The CCC values of all first-order and second-order GLCM features (except GLCM-energy) were all above 0.80 from the 2-day part test–retest set, while the CCC values all dropped below the cutoff after 5-day treatment scans. All first-order histogram-based and GLCM-texture-based features were not highly correlated with volume, while two GLRLM features, in both H&N and pelvic cohorts, showed R2 ≥0.8, meaning a high correlation with volume. Conclusion The reproducibility and repeatability of CBCT-based radiomics features were assessed and compared for the first time on both H&N and pelvic sites. There were overlaps of stable features in both disease sites, yet the overall stability of radiomics features may be disease-/protocol-specific and a function of time between scans.
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Affiliation(s)
- Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China.,Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States.,Institute of Modern Physics, Fudan University, Shanghai, China
| | - Yongkang Zhou
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Yin Zhang
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Chi Ma
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Bo Liu
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Qing Kong
- Institute of Modern Physics, Fudan University, Shanghai, China
| | - Ning Yue
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Zhiyong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Ke Nie
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
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21
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Nardone V, Reginelli A, Grassi R, Boldrini L, Vacca G, D'Ippolito E, Annunziata S, Farchione A, Belfiore MP, Desideri I, Cappabianca S. Delta radiomics: a systematic review. Radiol Med 2021; 126:1571-1583. [PMID: 34865190 DOI: 10.1007/s11547-021-01436-7] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/18/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches. METHODS Eligible articles were searched in Embase, PubMed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with three key search terms: "radiomics", "texture", and "delta". Studies were analysed using QUADAS-2 and the RQS tool. RESULTS Forty-eight studies were finally included. The studies were divided into preclinical/methodological (five studies, 10.4%); rectal cancer (six studies, 12.5%); lung cancer (twelve studies, 25%); sarcoma (five studies, 10.4%); prostate cancer (three studies, 6.3%), head and neck cancer (six studies, 12.5%); gastrointestinal malignancies excluding rectum (seven studies, 14.6%), and other disease sites (four studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%. CONCLUSIONS Delta radiomics shows potential benefit for several clinical endpoints in oncology (differential diagnosis, prognosis and prediction of treatment response, and evaluation of side effects). Nevertheless, the studies included in this systematic review suffer from the bias of overall low quality, so that the conclusions are currently heterogeneous, not robust, and not replicable. Further research with prospective and multicentre studies is needed for the clinical validation of delta radiomics approaches.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Luca Boldrini
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Giovanna Vacca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Emma D'Ippolito
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Salvatore Annunziata
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Alessandra Farchione
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Isacco Desideri
- Department of Biomedical, Experimental and Clinical Sciences "M. Serio", University of Florence, Florence, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
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22
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Morgan HE, Wang K, Dohopolski M, Liang X, Folkert MR, Sher DJ, Wang J. Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment cone-beam computed tomography features. Quant Imaging Med Surg 2021; 11:4781-4796. [PMID: 34888189 PMCID: PMC8611459 DOI: 10.21037/qims-21-274] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 06/28/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Local failure (LF) following chemoradiation (CRT) for head and neck cancer is associated with poor overall survival. If machine learning techniques could stratify patients at risk of treatment failure based on baseline and intra-treatment imaging, such a model could facilitate response-adapted approaches to escalate, de-escalate, or switch therapy. METHODS A 1:2 retrospective case control cohort of patients treated at a single institution with definitive radiotherapy for head and neck cancer who failed locally, in-field at a primary or nodal structure were included. Radiomic features were extracted from baseline CT and CBCT scans at fractions 1 and 21 (delta) of radiotherapy with PyRadiomics and were selected for by: reproducibility (intra-class correlation coefficients ≥0.95), redundancy [maximum relevance and minimum redundancy (mRMR)], and informativeness [recursive feature elimination (RFE)]. Separate models predicting LF of primaries or nodes were created using the explainable boosting machine (EBM) classifier with 5-fold cross-validation for (I) clinical only, (II) radiomic only (CT1 and delta features), and (III) fused models (clinical + radiomic). Twenty-five iterations were performed, and predicted scores were averaged with a parallel ensemble design. Receiver operating characteristic curves were compared between models with paired-samples t-tests. RESULTS The fused ensemble model for primaries (using clinical, CT1, and delta features) achieved an AUC of 0.871 with a sensitivity of 78.3% and specificity of 90.9% at the maximum Youden J statistic. The fused ensemble model trended towards improvement when compared to the clinical only ensemble model (AUC =0.788, P=0.134) but reached significance when compared to the radiomic ensemble model (AUC =0.770, P=0.017). The fused ensemble model for nodes achieved an AUC of 0.910 with a sensitivity of 100.0% and specificity of 68.0%, which also trended towards improvement when compared to the clinical model (AUC =0.865, P=0.080). CONCLUSIONS The fused ensemble EBM model achieved high discriminatory ability at predicting LF for head and neck cancer in independent primary and nodal structures. Although an additive benefit of delta radiomics over clinical factors could not be proven, the results trended towards improvement with the fused ensemble model, which are promising and worthy of prospective investigation in a larger cohort.
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Affiliation(s)
- Howard E. Morgan
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Kai Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Michael Dohopolski
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiao Liang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Michael R. Folkert
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - David J. Sher
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, USA
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23
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Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review. Clin Oncol (R Coll Radiol) 2021; 34:e107-e122. [PMID: 34763965 DOI: 10.1016/j.clon.2021.10.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/24/2021] [Accepted: 10/14/2021] [Indexed: 12/13/2022]
Abstract
Lung cancer's radiomic phenotype may potentially inform clinical decision-making with respect to radical radiotherapy. At present there are no validated biomarkers available for the individualisation of radical radiotherapy in lung cancer and the mortality rate of this disease remains the highest of all other solid tumours. MEDLINE was searched using the terms 'radiomics' and 'lung cancer' according to the Preferred Reporting Items for Systematic Reviews and Met-Analyses (PRISMA) guidance. Radiomics studies were defined as those manuscripts describing the extraction and analysis of at least 10 quantifiable imaging features. Only those studies assessing disease control, survival or toxicity outcomes for patients with lung cancer following radical radiotherapy ± chemotherapy were included. Study titles and abstracts were reviewed by two independent reviewers. The Radiomics Quality Score was applied to the full text of included papers. Of 244 returned results, 44 studies met the eligibility criteria for inclusion. End points frequently reported were local (17%), regional (17%) and distant control (31%), overall survival (79%) and pulmonary toxicity (4%). Imaging features strongly associated with clinical outcomes include texture features belonging to the subclasses Gray level run length matrix, Gray level co-occurrence matrix and kurtosis. The median cohort size for model development was 100 (15-645); in the 11 studies with external validation in a separate independent population, the median cohort size was 84 (21-295). The median number of imaging features extracted was 184 (10-6538). The median Radiomics Quality Score was 11% (0-47). Patient-reported outcomes were not incorporated within any studies identified. No studies externally validated a radiomics signature in a registered prospective study. Imaging-derived indices attained through radiomic analyses could equip thoracic oncologists with biomarkers for treatment response, patterns of failure, normal tissue toxicity and survival in lung cancer. Based on routine scans, their non-invasive nature and cost-effectiveness are major advantages over conventional pathological assessment. Improved tools are required for the appraisal of radiomics studies, as significant barriers to clinical implementation remain, such as standardisation of input scan data, quality of reporting and external validation of signatures in randomised, interventional clinical trials.
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24
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Iliadou V, Economopoulos TL, Karaiskos P, Kouloulias V, Platoni K, Matsopoulos GK. Deformable image registration to assist clinical decision for radiotherapy treatment adaptation for head and neck cancer patients. Biomed Phys Eng Express 2021; 7. [PMID: 34265756 DOI: 10.1088/2057-1976/ac14d1] [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: 05/05/2021] [Accepted: 07/15/2021] [Indexed: 11/12/2022]
Abstract
Head and neck (H&N) cancer patients often present anatomical and geometrical changes in tumors and organs at risk (OARs) during radiotherapy treatment. These changes may result in the need to adapt the existing treatment planning, using an expert's subjective opinion, for offline adaptive radiotherapy and a new treatment planning before each treatment, for online adaptive radiotherapy. In the present study, a fast methodology is proposed to assist in planning adaptation clinical decision using tumor and parotid glands percentage volume changes during treatment. The proposed approach was applied to 40 Η&Ν cases, with one planning Computed Tomography (pCT) image and CBCT scans for 6 weeks of treatment per case. Deformable registration was used for each patient's pCT image alignment to its weekly CBCT. The calculated transformations were used to align each patient's anatomical structures to the weekly anatomy. Clinical target volume (CTV) and parotid gland volume percentage changes were calculated in each case. The accuracy of the achieved image alignment was validated qualitatively and quantitatively. Furthermore, statistical analysis was performed to test if there is a statistically significant correlation between CTV and parotid glands volume percentage changes. Average MDA for CTV and parotid glands between corresponding structures defined by an expert in CBCTs and automatically calculated through registration was 1.4 ± 0.1 mm and 1.5 ± 0.1 mm, respectively. The mean registration time of the first CBCT image registration for 40 cases was lower than 3.4 min. Five patients show more than 20% tumor volume change. Six patients show more than 30% parotid glands volume change. Ten out of 40 patients proposed for planning adaptation. All the statistical tests performed showed no correlation between CTV/parotid glands percentage volume changes. The aim to assist in clinical decision making on a fast and automatic way was achieved using the proposed methodology, thereby reducing workload in clinical practice.
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Affiliation(s)
- Vasiliki Iliadou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Theodore L Economopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Pantelis Karaiskos
- Medical Physics Laboratory, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Vasileios Kouloulias
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, Athens, Greece
| | - Kalliopi Platoni
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, Athens, Greece
| | - George K Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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25
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Sun R, Lerousseau M, Henry T, Carré A, Leroy A, Estienne T, Niyoteka S, Bockel S, Rouyar A, Alvarez Andres É, Benzazon N, Battistella E, Classe M, Robert C, Scoazec JY, Deutsch É. [Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations]. Cancer Radiother 2021; 25:630-637. [PMID: 34284970 DOI: 10.1016/j.canrad.2021.06.027] [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: 06/08/2021] [Accepted: 06/19/2021] [Indexed: 12/24/2022]
Abstract
Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.
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Affiliation(s)
- R Sun
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France.
| | - M Lerousseau
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - T Henry
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de médecine nucléaire, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - A Carré
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - A Leroy
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - T Estienne
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Niyoteka
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Bockel
- Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - A Rouyar
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - É Alvarez Andres
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - N Benzazon
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - E Battistella
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | | | - C Robert
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - J Y Scoazec
- Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France; Département de biologie et pathologie médicales, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - É Deutsch
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
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26
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El Ayachy R, Giraud N, Giraud P, Durdux C, Giraud P, Burgun A, Bibault JE. The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up. Front Oncol 2021; 11:603595. [PMID: 34026602 PMCID: PMC8131863 DOI: 10.3389/fonc.2021.603595] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 04/06/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose Lung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients’ care. Methods A literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020 Results We identified several studies at each point of patient’s care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications. Conclusion Promising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.
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Affiliation(s)
- Radouane El Ayachy
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Nicolas Giraud
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France.,Radiation Oncology Department, Haut-Lévêque Hospital, CHU de Bordeaux, Pessac, France
| | - Paul Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Catherine Durdux
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Philippe Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Anita Burgun
- Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Jean Emmanuel Bibault
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
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27
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Artificial Intelligence and the Medical Physicist: Welcome to the Machine. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041691] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
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Dosimetric Factors and Radiomics Features Within Different Regions of Interest in Planning CT Images for Improving the Prediction of Radiation Pneumonitis. Int J Radiat Oncol Biol Phys 2021; 110:1161-1170. [PMID: 33548340 DOI: 10.1016/j.ijrobp.2021.01.049] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 11/21/2020] [Accepted: 01/24/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE This study aimed to establish machine learning models using dosimetric factors and radiomics features within 5 regions of interest (ROIs) in treatment planning computed tomography images to improve the prediction of symptomatic radiation pneumonitis (RP) (grade ≥2). METHODS AND MATERIALS This study retrospectively collected data on 79 patients with lung cancer (25 RP ≥2) who underwent chemoradiotherapy between 2015 and 2018. We defined 5 ROIs in planning computed tomography images: gross tumor volume (GTV), planning tumor volume (PTV), PTV-GTV, total lung (TL)-GTV, and TL-PTV. We calculated the mean dose, V5, V10, V20, and V30 within TL-GTV and TL-PTV and the mean dose within the other ROIs. A total of 1924 radiomics features were extracted from all 5 ROIs. We selected the best predictors for classifying 2 groups of patients using a sequential backward elimination support vector machine model. A permutation test was used to assess its statistical significance (P < .05). RESULTS The best predictors for symptomatic RP were the combination of 11 radiomics features, 5 dosimetric factors, age, and T stage, achieving an area under the curve (AUC) of 0.94 (95% confidence interval [CI], 0.85-1) (accuracy, 90%; sensitivity, 80% [95% CI, 44%-96%]; specificity, 95% [95% CI, 73%-100%]; P = 8 × 10-4). The clinical characteristics, dosimetric factors, and their combination showed limited predictive power (accuracy, 63.3%, 70%, and 70%; AUC [95% CI]: 0.73 [0.54-0.92], 0.53 [0.31-0.75], and 0.72 [0.51-0.92], respectively). The radiomics features of PTV-GTV and TL-PTV outperformed those of the other ROIs (accuracy, 76.7% and 76.7%; AUC [95% CI]: 0.82 [0.65-0.99] and 0.80 [0.59-1], respectively). CONCLUSIONS Combining dosimetric factors and radiomics features within different ROIs can improve the prediction of symptomatic RP. Our results can help physicians adjust the radiation dose distribution of the dose-sensitive lungs and target volumes based on personalized RP estimates.
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Byun DJ, Tam MM, Jacobson AS, Persky MS, Tran TT, Givi B, DeLacure MD, Li Z, Harrison LB, Hu KS. Prognostic potential of mid-treatment nodal response in oropharyngeal squamous cell carcinoma. Head Neck 2020; 43:10.1002/hed.26467. [PMID: 32964574 PMCID: PMC9879731 DOI: 10.1002/hed.26467] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/04/2020] [Accepted: 09/04/2020] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND We examine the prognostic implications of mid-course nodal response in oropharyngeal cancer (OPX) to radiation therapy. METHODS In 44 patients with node-positive OPX undergoing concurrent chemoradiation, nodal volumes were measured on cone beam CTs from days 1, 10, 20, and 35. Nodal decrease (ND) was based on percent shrinkage from day 1. RESULTS At a median follow-up of 17 months, the 2-year disease-free survival (DFS), locoregional control (LRC), distant metastasis-free survival (DMFS), and overall survival (OS) were 87%, 92%, 89%, and 92%, respectively. Patients with ND ≥43% at D20 had improved LRC (100% vs 78.4%, P = .03) compared to D20 ND <43%. On multivariate analysis, D20 ≥43% was independently prognostic for LRC (HR 1.17, P = .05). CONCLUSION Patients with low-risk oropharynx cancer with ND of ≥43% by treatment day 20 had significantly improved LRC. The prognostic benefit of ND may assist in identifying candidates for treatment de-escalation.
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Affiliation(s)
- David J. Byun
- Department of Radiation Oncology, NYU Langone Health, New York, New York
| | - Moses M. Tam
- Department of Radiation Oncology, NYU Langone Health, New York, New York
| | - Adam S. Jacobson
- Department of Otolaryngology, NYU Langone Health, New York, New York
| | - Mark S. Persky
- Department of Otolaryngology, NYU Langone Health, New York, New York
| | - Theresa T. Tran
- Department of Otolaryngology, NYU Langone Health, New York, New York
| | - Babak Givi
- Department of Otolaryngology, NYU Langone Health, New York, New York
| | - Mark D. DeLacure
- Department of Otolaryngology, NYU Langone Health, New York, New York
| | - Zujun Li
- Department of Medical Oncology, NYU Langone Health, New York, New York
| | - Louis B. Harrison
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Kenneth S. Hu
- Department of Radiation Oncology, NYU Langone Health, New York, New York
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30
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Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma. Mol Imaging Biol 2020; 22:1581-1591. [DOI: 10.1007/s11307-020-01507-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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31
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Fornacon-Wood I, Faivre-Finn C, O'Connor JPB, Price GJ. Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype. Lung Cancer 2020; 146:197-208. [PMID: 32563015 PMCID: PMC7383235 DOI: 10.1016/j.lungcan.2020.05.028] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/18/2020] [Accepted: 05/23/2020] [Indexed: 12/24/2022]
Abstract
Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.
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Affiliation(s)
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - James P B O'Connor
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - Gareth J Price
- Division of Cancer Sciences, University of Manchester, Manchester, UK
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Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol 2020; 196:879-887. [PMID: 32367456 DOI: 10.1007/s00066-020-01625-9] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 04/15/2020] [Indexed: 02/07/2023]
Abstract
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy.
| | | | - Giovanni Pirrone
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
| | - Giovanna Sartor
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
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Qin Q, Shi A, Zhang R, Wen Q, Niu T, Chen J, Qiu Q, Wan Y, Sun X, Xing L. Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. Thorac Cancer 2020; 11:964-972. [PMID: 32061061 PMCID: PMC7113065 DOI: 10.1111/1759-7714.13349] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 12/15/2022] Open
Abstract
Background Stereotactic body radiotherapy (SBRT) is the standard care for inoperable early stage non‐small cell lung cancer (NSCLC). The purpose of our study was to investigate whether a prediction model based on cone‐beam CT (CBCT) plus pretreatment CT radiomics features could improve the prediction of tumor control and lung toxicity after SBRT in comparison to a model based on pretreatment CT radiomics features alone. Methods A total of 34 cases of stage I NSCLC patients who received SBRT were included in the study. The pretreatment planning CT and serial CBCT radiomics features were analyzed using the imaging biomarker explorer (IBEX) software platform. Multivariate logistic regression was conducted for the association between progression‐free survival (PFS), lung toxicity and features. The predictive capabilities of the models based on CBCT and CT features were compared using receiver operating characteristic (ROC) curves. Results Five CBCT features and two planning CT features were correlated with disease progression. Six CBCT features and two planning CT features were related to lung injury. The ROC curves indicated that the model based on the CBCT plus planning CT features might be better than the model based on the planning CT features in predicting lung injury. The other ROC curves indicated that the model based on the planning CT features was similar to the model based on the CBCT plus planning CT features in predicting disease progression. Conclusions Both pretreatment CT and CBCT radiomics features could predict disease progression and lung injury. A model with CBCT plus pretreatment CT radiomics features might improve the prediction of lung toxicity in comparison with a model with pretreatment CT features alone. Key points Significant findings of the study: A model with cone‐beam CT radiomics features plus pre‐treatment CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. What this study adds: In the prediction of PFS and lung toxicity in early‐stage NSCLC patients treated with SBRT, CBCT radiomics could be another effective method.
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Affiliation(s)
- Qingjin Qin
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Anhui Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.,Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Ran Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China.,Shandong University Cheeloo College of Medicine, Jinan, China
| | - Qiang Wen
- Department of Oncology, Shandong Provincial Hospital, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Tianye Niu
- Nuclear & Radiological Engineering and Medical Physics Programs Woodruff School of Mechanical Engineering Georgia Institute of Technology, Atlanta, Georgia
| | - Jinhu Chen
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Yidong Wan
- Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang
| | - Xiaorong Sun
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Shandong University Cheeloo College of Medicine, Jinan, China.,Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Ligang Xing
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
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Yuan N, Dyer B, Rao S, Chen Q, Benedict S, Shang L, Kang Y, Qi J, Rong Y. Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy. Phys Med Biol 2020; 65:035003. [PMID: 31842014 PMCID: PMC8011532 DOI: 10.1088/1361-6560/ab6240] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
To improve image quality and CT number accuracy of fast-scan low-dose cone-beam computed tomography (CBCT) through a deep-learning convolutional neural network (CNN) methodology for head-and-neck (HN) radiotherapy. Fifty-five paired CBCT and CT images from HN patients were retrospectively analysed. Among them, 15 patients underwent adaptive replanning during treatment, thus had same-day CT/CBCT pairs. The remaining 40 patients (post-operative) had paired planning CT and 1st fraction CBCT images with minimal anatomic changes. A 2D U-Net architecture with 27-layers in 5 depths was built for the CNN. CNN training was performed using data from 40 post-operative HN patients with 2080 paired CT/CBCT slices. Validation and test datasets include 5 same-day datasets with 260 slice pairs and 10 same-day datasets with 520 slice pairs, respectively. To examine the impact of differences in training dataset selection and network performance as a function of training data size, additional networks were trained using 30, 40 and 50 datasets. Image quality of enhanced CBCT images were quantitatively compared against the CT image using mean absolute error (MAE) of Hounsfield units (HU), signal-to-noise ratio (SNR) and structural similarity (SSIM). Enhanced CBCT images reduced artifact distortion and improved soft tissue contrast. Networks trained with 40 datasets had imaging performance comparable to those trained with 50 datasets and outperformed those trained with 30 datasets. Comparison of CBCT and enhanced CBCT images demonstrated improvement in average MAE from 172.73 to 49.28 HU, SNR from 8.27 to 14.25 dB, and SSIM from 0.42 to 0.85. The image processing time is 2 s per patient using a NVIDIA GeForce GTX 1080 Ti GPU. The proposed deep-leaning methodology was fast and effective for image quality enhancement of fast-scan low-dose CBCT. This method has potential to support fast online-adaptive re-planning for HN cancer patients.
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Affiliation(s)
- Nimu Yuan
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, People's Republic of China
- Department of Biomedical Engineering, University of California, Davis, CA, United States of America
| | - Brandon Dyer
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States of America
- Department of Radiation Oncology, University of Washington, Seattle, WA, United States of America
| | - Shyam Rao
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States of America
| | - Quan Chen
- Department of Radiation Medicine, University of Kentucky, Lexington, KY, United States of America
| | - Stanley Benedict
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States of America
| | - Lu Shang
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States of America
| | - Yan Kang
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, People's Republic of China
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California, Davis, CA, United States of America
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States of America
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Fan Y, Jiang S, Hua M, Feng S, Feng M, Wang R. Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients With Acromegaly. Front Endocrinol (Lausanne) 2019; 10:588. [PMID: 31507537 PMCID: PMC6718446 DOI: 10.3389/fendo.2019.00588] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 08/12/2019] [Indexed: 12/29/2022] Open
Abstract
Background: Prediction of radiotherapeutic response before radiotherapy could help determine individual treatment strategies for patients with acromegaly. Objective: To develop and validate a machine-learning-based multiparametric MRI radiomics model to non-invasively predict radiotherapeutic response in patients with acromegaly. Methods: This retrospective study included 57 acromegaly patients who underwent postoperative radiotherapy between January 2008 and January 2016. Manual lesion segmentation and radiomics analysis were performed on each pituitary adenoma, and 1561 radiomics features were extracted from each sequence. A radiomics signature was built with a support vector machine using leave-one-out cross-validation for feature selection. Multivariable logistic regression analysis was used to select appropriate clinicopathological features to construct a clinical model, which was then combined with the radiomics signature to construct a radiomics model. The performance of this radiomic model was assessed using receiver operating characteristics (ROC) analysis and its calibration, discriminating ability, clinical usefulness. Results: At 3-years after radiotherapy, 25 patients had achieved remission and 32 patients had not. The clinical model incorporating seven clinical features had an area under the ROC (AUC) of 0.86 for predicting radiotherapeutic response, and performed better than any single clinical feature. The radiomics signature constructed with six radiomics features had a significantly higher AUC of 0.92. The radiomics model showed good discrimination abilities and calibration, with an AUC of 0.96. Decision curve analysis confirmed the clinical utility of the radiomics model. Conclusion: Using pre-radiotherapy clinical and MRI data, we developed a radiomics model with favorable performance for individualized non-invasive prediction of radiotherapeutic response, which may help in identifying acromegaly patients who are likely to benefit from radiotherapy.
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Affiliation(s)
- Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shenzhong Jiang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Min Hua
- School of Electrical Engineering and Automation, East China Jiaotong University, Nanchang, China
| | - Shanshan Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Renzhi Wang
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Ming Feng ;
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