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Boldrini L, Chiloiro G, Cusumano D, Yadav P, Yu G, Romano A, Piras A, Votta C, Placidi L, Broggi S, Catucci F, Lenkowicz J, Indovina L, Bassetti MF, Yang Y, Fiorino C, Valentini V, Gambacorta MA. Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study. LA RADIOLOGIA MEDICA 2024; 129:615-622. [PMID: 38512616 DOI: 10.1007/s11547-024-01761-7] [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: 06/15/2023] [Accepted: 01/03/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE The accurate prediction of treatment response in locally advanced rectal cancer (LARC) patients undergoing MRI-guided radiotherapy (MRIgRT) is essential for optimising treatment strategies. This multi-institutional study aimed to investigate the potential of radiomics in enhancing the predictive power of a known radiobiological parameter (Early Regression Index, ERITCP) to evaluate treatment response in LARC patients treated with MRIgRT. METHODS Patients from three international sites were included and divided into training and validation sets. 0.35 T T2*/T1-weighted MR images were acquired during simulation and at each treatment fraction. The biologically effective dose (BED) conversion was used to account for different radiotherapy schemes: gross tumour volume was delineated on the MR images corresponding to specific BED levels and radiomic features were then extracted. Multiple logistic regression models were calculated, combining ERITCP with other radiomic features. The predictive performance of the different models was evaluated on both training and validation sets by calculating the receiver operating characteristic (ROC) curves. RESULTS A total of 91 patients was enrolled: 58 were used as training, 33 as validation. Overall, pCR was observed in 25 cases. The model showing the highest performance was obtained combining ERITCP at BED = 26 Gy with a radiomic feature (10th percentile of grey level histogram, 10GLH) calculated at BED = 40 Gy. The area under ROC curve (AUC) of this combined model was 0.98 for training set and 0.92 for validation set, significantly higher (p = 0.04) than the AUC value obtained using ERITCP alone (0.94 in training and 0.89 in validation set). CONCLUSION The integration of the radiomic analysis with ERITCP improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35 T MRIgRT treatments of LARC patients.
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Affiliation(s)
- Luca Boldrini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | | | - Poonam Yadav
- Northwestern Memorial Hospital, Northwestern University Feinberg, Chicago, IL, USA
| | - Gao Yu
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Angela Romano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy
| | - Claudio Votta
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | | | - Jacopo Lenkowicz
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Michael F Bassetti
- Department of Human Oncology, School of Medicine and Public Heath, University of Wisconsin - Madison, Madison, USA
| | - Yingli Yang
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
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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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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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Chauvie S, Mazzoni LN, O’Doherty J. A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation. Tomography 2023; 9:1876-1902. [PMID: 37888741 PMCID: PMC10610870 DOI: 10.3390/tomography9050149] [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: 08/16/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
Imaging biomarkers (IBs) have been proposed in medical literature that exploit images in a quantitative way, going beyond the visual assessment by an imaging physician. These IBs can be used in the diagnosis, prognosis, and response assessment of several pathologies and are very often used for patient management pathways. In this respect, IBs to be used in clinical practice and clinical trials have a requirement to be precise, accurate, and reproducible. Due to limitations in imaging technology, an error can be associated with their value when considering the entire imaging chain, from data acquisition to data reconstruction and subsequent analysis. From this point of view, the use of IBs in clinical trials requires a broadening of the concept of quality assurance and this can be a challenge for the responsible medical physics experts (MPEs). Within this manuscript, we describe the concept of an IB, examine some examples of IBs currently employed in clinical practice/clinical trials and analyze the procedure that should be carried out to achieve better accuracy and reproducibility in their use. We anticipate that this narrative review, written by the components of the EFOMP working group on "the role of the MPEs in clinical trials"-imaging sub-group, can represent a valid reference material for MPEs approaching the subject.
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Affiliation(s)
- Stephane Chauvie
- Medical Physics Division, Santa Croce e Carle Hospital, 12100 Cuneo, Italy;
| | | | - Jim O’Doherty
- Siemens Medical Solutions, Malvern, PA 19355, USA;
- Department of Radiology & Radiological Sciences, Medical University of South Carolina, Charleston, SC 20455, USA
- Radiography & Diagnostic Imaging, University College Dublin, D04 C7X2 Dublin, Ireland
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Kimura C, Crowder SE, Kin C. Is It Really Gone? Assessing Response to Neoadjuvant Therapy in Rectal Cancer. J Gastrointest Cancer 2023; 54:703-711. [PMID: 36417142 DOI: 10.1007/s12029-022-00889-x] [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] [Accepted: 11/13/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE Non-operative management of rectal cancer is a feasible and appealing treatment option for patients who develop a complete response after neoadjuvant therapy. However, identifying patients who are complete responders is often a challenge. This review aims to present and discuss current evidence and recommendations regarding the assessment of treatment response in rectal cancer. METHODS A review of the current literature on rectal cancer restaging was performed. Studies included in this review explored the optimal interval between the end of neoadjuvant therapy and restaging, as well as modalities of assessment and their diagnostic performance. RESULTS The current standard for restaging rectal cancer is a multimodal assessment with the digital rectal examination, endoscopy, and T2-weighted MRI with diffusion-weighted imaging. Other diagnostic procedures under investigation are PET/MRI, radiomics, confocal laser endomicroscopy, artificial intelligence-assisted endoscopy, cell-free DNA, and prediction models incorporating one or more of the above-mentioned exams. CONCLUSION Non-operative management of rectal cancer requires a multidisciplinary approach. Understanding of the robustness and limitations of each exam is critical to inform patient selection for that treatment strategy.
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Affiliation(s)
- Cintia Kimura
- Department of Surgery, Division of General Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, H3680K94305, USA
| | - Sarah Elizabeth Crowder
- Department of Surgery, Division of General Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, H3680K94305, USA
- Brigham Young University, Provo, UT, USA
| | - Cindy Kin
- Department of Surgery, Division of General Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, H3680K94305, USA.
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Di Costanzo G, Ascione R, Ponsiglione A, Tucci AG, Dell’Aversana S, Iasiello F, Cavaglià E. Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:406-421. [PMID: 37455833 PMCID: PMC10344900 DOI: 10.37349/etat.2023.00142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 03/01/2023] [Indexed: 07/18/2023] Open
Abstract
Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.
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Affiliation(s)
- Giuseppe Di Costanzo
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Raffaele Ascione
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Anna Giacoma Tucci
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Serena Dell’Aversana
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Francesca Iasiello
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Enrico Cavaglià
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
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Chiloiro G, Cusumano D, Romano A, Boldrini L, Nicolì G, Votta C, Tran HE, Barbaro B, Carano D, Valentini V, Gambacorta MA. Delta Radiomic Analysis of Mesorectum to Predict Treatment Response and Prognosis in Locally Advanced Rectal Cancer. Cancers (Basel) 2023; 15:3082. [PMID: 37370692 DOI: 10.3390/cancers15123082] [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: 04/28/2023] [Revised: 05/23/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND The aim of this study is to evaluate the delta radiomics approach based on mesorectal radiomic features to develop a model for predicting pathological complete response (pCR) and 2-year disease-free survival (2yDFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (nCRT). METHODS Pre- and post-nCRT MRIs of LARC patients treated at a single institution from May 2008 to November 2016 were retrospectively collected. Radiomic features were extracted from the GTV and mesorectum. The Wilcoxon-Mann-Whitney test and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the features in predicting pCR and 2yDFS. RESULTS Out of 203 LARC patients, a total of 565 variables were evaluated. The best performing pCR prediction model was based on two GTV features with an AUC of 0.80 in the training set and 0.69 in the validation set. The best performing 2yDFS prediction model was based on one GTV and two mesorectal features with an AUC of 0.79 in the training set and 0.70 in the validation set. CONCLUSIONS The results of this study suggest a possible role for delta radiomics based on mesorectal features in the prediction of 2yDFS in patients with LARC.
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Affiliation(s)
- Giuditta Chiloiro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Davide Cusumano
- Mater Olbia Hospital, Strada Statale Orientale Sarda 125, 07026 Olbia, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Giuseppe Nicolì
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Claudio Votta
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Huong Elena Tran
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Brunella Barbaro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Davide Carano
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
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Liu Y, Wei X, Feng X, Liu Y, Feng G, Du Y. Repeatability of radiomics studies in colorectal cancer: a systematic review. BMC Gastroenterol 2023; 23:125. [PMID: 37059990 PMCID: PMC10105401 DOI: 10.1186/s12876-023-02743-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/22/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer. METHODS The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies. RESULTS A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27. CONCLUSIONS The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.
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Affiliation(s)
- Ying Liu
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | - Xiaoqin Wei
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | | | - Yan Liu
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Guiling Feng
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Yong Du
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China.
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Xue C, Chu WCW, Yuan J, Poon DMC, Yang B, Zhou Y, Yu SK, Cheung KY. Determining the reliable feature change in longitudinal radiomics studies: A methodological approach using the reliable change index. Med Phys 2023; 50:958-969. [PMID: 36251320 DOI: 10.1002/mp.16046] [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: 04/07/2022] [Revised: 07/28/2022] [Accepted: 09/30/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Determination of reliable change of radiomics feature over time is essential and vital in delta-radiomics, but has not yet been rigorously examined. This study attempts to propose a methodological approach using reliable change index (RCI), a statistical metric to determine the reliability of quantitative biomarker changes by accounting for the baseline measurement standard error, in delta-radiomics. The use of RCI was demonstrated with the MRI data acquired from a group of prostate cancer (PCa) patients treated by 1.5 T MRI-guided radiotherapy (MRgRT). METHODS Fifty consecutive PCa patients who underwent five-fractionated MRgRT were retrospectively included, and 1023 radiomics features were extracted from the clinical target volume (CTV) and planning target volume (PTV). The two MRI datasets acquired at the first fraction (MRI11 and MRI21) were used to calculate the baseline feature reliability against image acquisition using intraclass correlation coefficient (ICC). The RCI was constructed based on the baseline feature measurement standard deviation, ICC, and feature value differences at two time points between the fifth (MRI51) and the first fraction MRI (MRI11). The reliable change of features was determined in each patient only if the calculated RCI was over 1.96 or smaller than -1.96. The feature changes between MRI51 and MRI11 were correlated to two patient-reported quality-of-life clinical endpoints of urinary domain summary score (UDSS) and bowel domain summary score (BDSS) in 35 patients using the Spearman correlation test. Only the significant correlations between a feature that was reliably changed in ≥7 patients (20%) by RCI and an endpoint were considered as true significant correlations. RESULTS The 352 (34.4%) and 386 (37.7%) features among all 1023 features were determined by RCI to be reliably changed in more than five (10%) patients in the CTV and PTV, respectively. Nineteen features were found reliably changed in the CTV and 31 features in the PTV, respectively, in 10 (20%) or more patients. These features were not necessarily associated with significantly different longitudinal feature values (group p-value < 0.05). Most reliably changed features in more than 10 patients had excellent or good baseline test-retest reliability ICC, while none showed poor reliability. The RCI method ruled out the features to be reliably changed when substantial feature measurement bias was presented. After applying the RCI criterion, only four and five true significant correlations were confirmed with UDSS and BDSS in the CTV, respectively, with low true significance correlation rates of 10.8% (4/37) and 17.9% (5/28). No true significant correlations were found in the PTV. CONCLUSIONS The RCI method was proposed for delta-radiomics and demonstrated using PCa MRgRT data. The RCI has advantages over some other statistical metrics commonly used in the previous delta-radiomics studies, and is useful to reliably identify the longitudinal radiomics feature change on an individual basis. This proposed RCI method should be helpful for the development of essential feature selection methodology in delta-radiomics.
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Affiliation(s)
- Cindy Xue
- Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China.,Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jing Yuan
- Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Darren M C Poon
- Comprehensive Oncology Center, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Bin Yang
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Yihang Zhou
- Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Siu Ki Yu
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Kin Yin Cheung
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
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Ng J, Gregucci F, Pennell RT, Nagar H, Golden EB, Knisely JPS, Sanfilippo NJ, Formenti SC. MRI-LINAC: A transformative technology in radiation oncology. Front Oncol 2023; 13:1117874. [PMID: 36776309 PMCID: PMC9911688 DOI: 10.3389/fonc.2023.1117874] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/16/2023] [Indexed: 01/28/2023] Open
Abstract
Advances in radiotherapy technologies have enabled more precise target guidance, improved treatment verification, and greater control and versatility in radiation delivery. Amongst the recent novel technologies, Magnetic Resonance Imaging (MRI) guided radiotherapy (MRgRT) may hold the greatest potential to improve the therapeutic gains of image-guided delivery of radiation dose. The ability of the MRI linear accelerator (LINAC) to image tumors and organs with on-table MRI, to manage organ motion and dose delivery in real-time, and to adapt the radiotherapy plan on the day of treatment while the patient is on the table are major advances relative to current conventional radiation treatments. These advanced techniques demand efficient coordination and communication between members of the treatment team. MRgRT could fundamentally transform the radiotherapy delivery process within radiation oncology centers through the reorganization of the patient and treatment team workflow process. However, the MRgRT technology currently is limited by accessibility due to the cost of capital investment and the time and personnel allocation needed for each fractional treatment and the unclear clinical benefit compared to conventional radiotherapy platforms. As the technology evolves and becomes more widely available, we present the case that MRgRT has the potential to become a widely utilized treatment platform and transform the radiation oncology treatment process just as earlier disruptive radiation therapy technologies have done.
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Affiliation(s)
- John Ng
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States,*Correspondence: John Ng,
| | - Fabiana Gregucci
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States,Department of Radiation Oncology, Miulli General Regional Hospital, Acquaviva delle Fonti, Bari, Italy
| | - Ryan T. Pennell
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States
| | - Himanshu Nagar
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States
| | - Encouse B. Golden
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States
| | | | | | - Silvia C. Formenti
- Department of Radiation Oncology, Weill Cornell Medicine, New York, NY, United States
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11
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Nardini M, Capotosti A, Mazzoni LN, Cusumano D, Boldrini L, Chiloiro G, Romano A, Valentini V, Indovina L, Placidi L. Tuning the optimal diffusion-weighted MRI parameters on a 0.35-T MR-Linac for clinical implementation: A phantom study. Front Oncol 2022; 12:867792. [PMID: 36523999 PMCID: PMC9745186 DOI: 10.3389/fonc.2022.867792] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 11/07/2022] [Indexed: 12/06/2023] Open
Abstract
PURPOSE This study aims to assess the quality of a new diffusion-weighted imaging (DWI) sequence implemented on an MR-Linac MRIdian system, evaluating and optimizing the acquisition parameters to explore the possibility of clinically implementing a DWI acquisition protocol in a 0.35-T MR-Linac. MATERIALS AND METHODS All the performed analyses have been carried out on two types of phantoms: a homogeneous 24-cm diameter polymethylmethacrylate (PMMA) sphere (SP) and a homemade phantom (HMP) constating in a PMMA cylinder filled with distilled water with empty sockets into which five cylindrical vials filled with five different concentrations of methylcellulose water solutions have been inserted. SP was used to evaluate the dependence of diffusion gradient inhomogeneity artifacts on gantry position. Four diffusion sequences with b-values of 500 s/mm2 and 3 averages have been acquired: three with diffusion gradients in the three main directions (phase direction, read direction, slice direction) and one with the diffusion gradients switched off. The dependence of diffusion image uniformity and SNR on the number of averages in the MR sequences was also investigated to determine the optimal number of averages. Finally, the ADC values of HMP have been computed and then compared between images acquired in the scanners at 0.35 and 1.5 T. RESULTS In order to acquire high-quality artifact-free DWI images, the "slice" gradient direction has been identified to be the optimal one and 0° to be the best gradient angle. Both the SNR ratio and the uniformity increase with the number of averages. A threshold value of 80 for SNR and 85% for uniformity was adopted to choose the best number of averages. By making a compromise between time and quality and limiting the number of b-values, it is possible to reduce the acquisition time to 78 s. The Passing-Bablok test showed that the two methods, with 0.35 and 1.5 T scanners, led to similar results. CONCLUSION The quality of the DWI has been accurately evaluated in relation to different sequence parameters, and optimal parameters have been identified to select a clinical protocol for the acquisition of ADC maps sustainable in the workflow of a hybrid radiotherapy system with a 0.35-T MRI scanner.
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Affiliation(s)
- Matteo Nardini
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Amedeo Capotosti
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Lorenzo Nicola Mazzoni
- Azienda Unità Sanitaria Locale (AUSL) Toscana Centro, Medical Physics Unit, Prato-Pistoia, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
- Mater Olbia Hospital, UOS Fisica Medica, Olbia, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
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Chen R, Fu Y, Yi X, Pei Q, Zai H, Chen BT. Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges. JOURNAL OF ONCOLOGY 2022; 2022:1590620. [PMID: 36471884 PMCID: PMC9719428 DOI: 10.1155/2022/1590620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/30/2022] [Accepted: 11/09/2022] [Indexed: 08/01/2023]
Abstract
Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.
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Affiliation(s)
- Rui Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Qian Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Hongyan Zai
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Bihong T. Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
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MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy. Sci Rep 2022; 12:18631. [PMID: 36329116 PMCID: PMC9633752 DOI: 10.1038/s41598-022-22826-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
Real-time magnetic resonance image guided stereotactic ablative radiotherapy (MRgSBRT) is used to treat abdominal tumors. Longitudinal data is generated from daily setup images. Our study aimed to identify delta radiomic texture features extracted from these images to predict for local control in patients with liver tumors treated with MRgSBRT. Retrospective analysis of an IRB-approved database identified patients treated with MRgSBRT for primary liver and secondary metastasis histologies. Daily low field strength (0.35 T) images were retrieved, and the gross tumor volume was identified on each image. Next, images' gray levels were equalized, and 39 s-order texture features were extracted. Delta-radiomics were calculated as the difference between feature values on the initial scan and after delivered biological effective doses (BED, α/β = 10) of 20 Gy and 40 Gy. Then, features were ranked by the Gini Index during training of a random forest model. Finally, the area under the receiver operating characteristic curve (AUC) was estimated using a bootstrapped logistic regression with the top two features. We identified 22 patients for analysis. The median dose delivered was 50 Gy in 5 fractions. The top two features identified after delivery of BED 20 Gy were gray level co-occurrence matrix features energy and gray level size zone matrix based large zone emphasis. The model generated an AUC = 0.9011 (0.752-1.0) during bootstrapped logistic regression. The same two features were selected after delivery of a BED 40 Gy, with an AUC = 0.716 (0.600-0.786). Delta-radiomic features after a single fraction of SBRT predicted local control in this exploratory cohort. If confirmed in larger studies, these features may identify patients with radioresistant disease and provide an opportunity for physicians to alter management much sooner than standard restaging after 3 months. Expansion of the patient database is warranted for further analysis of delta-radiomic features.
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Qin Y, Zhu LH, Zhao W, Wang JJ, Wang H. Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer. Front Oncol 2022; 12:913683. [PMID: 36016617 PMCID: PMC9395725 DOI: 10.3389/fonc.2022.913683] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/23/2022] [Indexed: 12/20/2022] Open
Abstract
By breaking the traditional medical image analysis framework, precision medicine–radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially 125I CT-guided radioactive seed implant brachytherapy.
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Affiliation(s)
- Yun Qin
- School of Physics, Beihang University, Beijing, China
| | - Li-Hua Zhu
- School of Physics, Beihang University, Beijing, China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing, China
| | - Jun-Jie Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
- *Correspondence: Jun-Jie Wang, ; Hao Wang,
| | - Hao Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
- *Correspondence: Jun-Jie Wang, ; Hao Wang,
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15
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Xiao Y, Qiu M, Huang W, Hu S, Tan C, Nan F, Jiang X, Wu D, Li M, Li Q, Qin C. Global status of research on radiotherapy for rectal cancer: A bibliometric and visual analysis. Front Public Health 2022; 10:962256. [PMID: 36003628 PMCID: PMC9393343 DOI: 10.3389/fpubh.2022.962256] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/11/2022] [Indexed: 12/24/2022] Open
Abstract
Radiotherapy for rectal cancer has received increasing research attention in recent years; however, no bibliometric assessment has been conducted on the progress of research in this field. This study aimed to visualize the research evolution and emerging research hotspots in the field of rectal cancer radiotherapy using bibliometric methods. Data were collected from the Web of Science Core Collection database, including countries, institutions, authors, keywords, and co-citations of references, and the CiteSpace software was used for bibliometric analysis. A total of 5,372 publications on radiotherapy for rectal cancer, published between January 2000 and January 2022, were included. An increasing trend in the number of published articles was observed. There is an overall upward trend in the number of publications published, with the US publishing the most in this field, followed by China and the Netherlands. Italian writer Vincenzo Valentini and German writer R. Sauer ranked first in terms of published articles and co-cited authors, respectively. Literature co-citation and keyword co-occurrence analyses showed that early studies focused on topics such as preoperative radiotherapy, combined radiotherapy and chemotherapy, and total mesorectal excision. In recent years, gradually increasing attention has been paid to short-course radiotherapy, x-ray brachytherapy, and stereotactic systemic radiotherapy. Burst analysis suggested that magnetic resonance (MR)-guided neoadjuvant radiotherapy studies, mechanistic studies, and clinical trials may emerge as new research hotspots. Rectal cancer radiotherapy has been widely studied and the research hotspots have considerably changed in recent years. Future research hotspots may include MR-guided neoadjuvant radiotherapy studies, mechanistic studies, and clinical trials.
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Affiliation(s)
- Yafei Xiao
- Department of Gastrointestinal Surgery, Huaihe Hospital of Henan University, Kaifeng, China
- Ruida Pharmaceutical Clinical Medicine Postgraduate Education Innovation Training Base of Henan University, Kaifeng, China
| | - Mengyuan Qiu
- Department of Neurology, Peking University People's Hospital, Peking University School of Medicine, Beijing, China
| | - Wanting Huang
- Department of Gynecology and Obstetrics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shaowen Hu
- Department of Gastrointestinal Surgery, Huaihe Hospital of Henan University, Kaifeng, China
| | - Cong Tan
- Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Fangmei Nan
- Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Xiaowei Jiang
- Department of Pediatric Orthopaedics, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dapeng Wu
- Department of Radiology, Huaihe Hospital of Henan University, Kaifeng, China
| | - Mengmeng Li
- Department of Gastrointestinal Surgery, Huaihe Hospital of Henan University, Kaifeng, China
| | - Quanying Li
- Department of Gastrointestinal Surgery, Huaihe Hospital of Henan University, Kaifeng, China
| | - Changjiang Qin
- Department of Gastrointestinal Surgery, Huaihe Hospital of Henan University, Kaifeng, China
- Ruida Pharmaceutical Clinical Medicine Postgraduate Education Innovation Training Base of Henan University, Kaifeng, China
- *Correspondence: Changjiang Qin
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Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery. Cancers (Basel) 2022; 14:cancers14123004. [PMID: 35740669 PMCID: PMC9221458 DOI: 10.3390/cancers14123004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/27/2022] [Accepted: 06/15/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary The present study aimed to investigate the possible use of MRI delta texture analysis (D-TA) in order to predict the extent of pathological response in patients with locally advanced rectal cancer addressed to neoadjuvant chemo-radiotherapy (C-RT) followed by surgery. We found that D-TA may really predict the frequency of pCR in this patient setting and, thus, it may be investigated as a potential item to identify candidate patients who may benefit from an aggressive radical surgery. Abstract We performed a pilot study to evaluate the use of MRI delta texture analysis (D-TA) as a methodological item able to predict the frequency of complete pathological responses and, consequently, the outcome of patients with locally advanced rectal cancer addressed to neoadjuvant chemoradiotherapy (C-RT) and subsequently, to radical surgery. In particular, we carried out a retrospective analysis including 100 patients with locally advanced rectal adenocarcinoma who received C-RT and then radical surgery in three different oncological institutions between January 2013 and December 2019. Our experimental design was focused on the evaluation of the gross tumor volume (GTV) at baseline and after C-RT by means of MRI, which was contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted by using a LifeX Software, while D-TA was calculated as percentage of variations in the two time points. Both univariate and multivariate analysis (logistic regression) were, therefore, carried out in order to correlate the above-mentioned TA parameters with the frequency of pathological responses in the examined patients’ population focusing on the detection of complete pathological response (pCR, with no viable cancer cells: TRG 1) as main statistical endpoint. ROC curves were performed on three different datasets considering that on the 21 patients, only 21% achieved an actual pCR. In our training dataset series, pCR frequency significantly correlated with ADC GLCM-Entropy only, when univariate and binary logistic analysis were performed (AUC for pCR was 0.87). A confirmative binary logistic regression analysis was then repeated in the two remaining validation datasets (AUC for pCR was 0.92 and 0.88, respectively). Overall, these results support the hypothesis that D-TA may have a significant predictive value in detecting the occurrence of pCR in our patient series. If confirmed in prospective and multicenter trials, these results may have a critical role in the selection of patients with locally advanced rectal cancer who may benefit form radical surgery after neoadjuvant chemoradiotherapy.
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Autorino R, Gui B, Panza G, Boldrini L, Cusumano D, Russo L, Nardangeli A, Persiani S, Campitelli M, Ferrandina G, Macchia G, Valentini V, Gambacorta MA, Manfredi R. Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy. Radiol Med 2022; 127:498-506. [PMID: 35325372 PMCID: PMC9098600 DOI: 10.1007/s11547-022-01482-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/08/2022] [Indexed: 12/31/2022]
Abstract
PURPOSE The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT). MATERIALS AND METHODS We retrospectively enrolled patients with LACC diagnosis who underwent NACRT followed by radical surgery in two different institutions. Radiomics features were extracted from pre-treatment 1.5 T T2w MR images. The predictive performance of each feature was quantified in terms of Wilcoxon-Mann-Whitney test. Among the significant features, Pearson correlation coefficient (PCC) was calculated to quantify the correlation among the different predictors. A logistic regression model was calculated considering the two most significant features at the univariate analysis showing the lowest PCC value. The predictive performance of the model created was quantified out using the area under the receiver operating characteristic curve (AUC). RESULTS A total of 175 patients were retrospectively enrolled (142 for the training cohort and 33 for the validation one). 1896 radiomic feature were extracted, 91 of which showed significance (p < 0.05) at the univariate analysis. The radiomic model showing the highest predictive value combined the features calculated starting from the gray level co-occurrence-based features. This model achieved an AUC of 0.73 in the training set and 0.91 in the validation set. CONCLUSIONS The proposed radiomic model showed promising performances in predicting 2-year overall survival before NACRT. Nevertheless, the observed results should be tested in larger studies with consistent external validation cohorts, to confirm their potential clinical use.
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Affiliation(s)
- Rosa Autorino
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Benedetta Gui
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Giulia Panza
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy.
| | - Luca Boldrini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy.,Mater Olbia Hospital, 07026, Olbia, SS, Italy
| | - Luca Russo
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Alessia Nardangeli
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Salvatore Persiani
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Maura Campitelli
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Gabriella Ferrandina
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Gabriella Macchia
- Gemelli Molise Hospital, Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy.,Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Maria Antonietta Gambacorta
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy.,Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Riccardo Manfredi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy.,Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
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Chiloiro G, Boldrini L, Preziosi F, Cusumano D, Yadav P, Romano A, Placidi L, Lenkowicz J, Dinapoli N, Bassetti MF, Gambacorta MA, Valentini V. A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients. Front Oncol 2022; 12:831712. [PMID: 35280799 PMCID: PMC8907443 DOI: 10.3389/fonc.2022.831712] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/31/2022] [Indexed: 01/04/2023] Open
Abstract
Purpose Distant metastasis is the main cause of treatment failure in locally advanced rectal cancer (LARC) patients, despite the recent improvement in treatment strategies. This study aims to evaluate the “delta radiomics” approach in patients undergoing neoadjuvant chemoradiotherapy (nCRT) treated with 0.35-T magnetic resonance-guided radiotherapy (MRgRT), developing a logistic regression model able to predict 2-year disease-free-survival (2yDFS). Methods Patients affected by LARC were enrolled in this multi-institutional study. A predictive model of 2yDFS was developed taking into account both clinical and radiomics variables. Gross tumour volume (GTV) was delineated on the magnetic resonance (MR) images acquired during MRgRT, and 1,067 radiomic features (RF) were extracted using the MODDICOM platform. The performance of RF in predicting 2yDFS was investigated in terms of the Wilcoxon–Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC). Results 48 patients have been retrospectively enrolled, with 8 patients (16.7%) developing distant metastases at the 2-year follow-up. A total of 1,099 variables (1,067 RF and 32 clinical variables) were evaluated in two different models: radiomics and radiomics/clinical. The best-performing 2yDFS predictive model was a delta radiomics one, based on the variation in terms of area/surface ratio between biologically effective doses (BED) at 54 Gy and simulation (AUC of 0.92). Conclusions The results of this study suggest a promising role of delta radiomics analysis on 0.35-T MR images in predicting 2yDFS for LARC patients. Further analyses including larger cohorts of patients and an external validation are needed to confirm these preliminary results.
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Affiliation(s)
- Giuditta Chiloiro
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Luca Boldrini
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Francesco Preziosi
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Davide Cusumano
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Poonam Yadav
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Angela Romano
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Lorenzo Placidi
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Jacopo Lenkowicz
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Nicola Dinapoli
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Michael F Bassetti
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Maria Antonietta Gambacorta
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Vincenzo Valentini
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
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Chiloiro G, Cusumano D, Boldrini L, Romano A, Placidi L, Nardini M, Meldolesi E, Barbaro B, Coco C, Crucitti A, Persiani R, Petruzziello L, Ricci R, Salvatore L, Sofo L, Alfieri S, Manfredi R, Valentini V, Gambacorta MA. THUNDER 2: THeragnostic Utilities for Neoplastic DisEases of the Rectum by MRI guided radiotherapy. BMC Cancer 2022; 22:67. [PMID: 35033008 PMCID: PMC8760695 DOI: 10.1186/s12885-021-09158-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/24/2021] [Indexed: 02/07/2023] Open
Abstract
Background Neoadjuvant chemoradiation therapy (nCRT) is the standard treatment modality in locally advanced rectal cancer (LARC). Since response to radiotherapy (RT) is dose dependent in rectal cancer, dose escalation may lead to higher complete response rates. The possibility to predict patients who will achieve complete response (CR) is fundamental. Recently, an early tumour regression index (ERI) was introduced to predict pathological CR (pCR) after nCRT in LARC patients. The primary endpoints will be the increase of CR rate and the evaluation of feasibility of delta radiomics-based predictive MRI guided Radiotherapy (MRgRT) model. Methods Patients affected by LARC cT2-3, N0-2 or cT4 for anal sphincter involvement N0-2a, M0 without high risk features will be enrolled in the trial. Neoadjuvant CRT will be administered using MRgRT. The initial RT treatment will consist in delivering 55 Gy in 25 fractions on Gross Tumor Volume (GTV) plus the corresponding mesorectum and 45 Gy in 25 fractions on the drainage nodes. Chemotherapy with 5-fluoracil (5-FU) or oral capecitabine will be administered continuously. A 0.35 Tesla MRI will be acquired at simulation and every day during MRgRT. At fraction 10, ERI will be calculated: if ERI will be inferior than 13.1, the patient will continue the original treatment; if ERI will be higher than 13.1 the treatment plan will be reoptimized, intensifying the dose to the residual tumor at the 11th fraction to reach 60.1 Gy. At the end of nCRT instrumental examinations are to be performed in order to restage patients. In case of stable disease or progression, the patient will undergo surgery. In case of major or complete clinical response, conservative approaches may be chosen. Patients will be followed up to evaluate toxicity and quality of life. The number of cases to be enrolled will be 63: all the patients will be treated at Fondazione Policlinico Universitario A. Gemelli IRCCS in Rome. Discussion This clinical trial investigates the impact of RT dose escalation in poor responder LARC patients identified using ERI, with the aim of increasing the probability of CR and consequently an organ preservation benefit in this group of patients. Trial registration ClinicalTrials.gov Identifier: NCT04815694 (25/03/2021).
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Affiliation(s)
- Giuditta Chiloiro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy.
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Matteo Nardini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Elisa Meldolesi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Brunella Barbaro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Claudio Coco
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Antonio Crucitti
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Roberto Persiani
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Lucio Petruzziello
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Riccardo Ricci
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Lisa Salvatore
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Luigi Sofo
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Sergio Alfieri
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Riccardo Manfredi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
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Hall WA, Paulson E, Li XA, Erickson B, Schultz C, Tree A, Awan M, Low DA, McDonald BA, Salzillo T, Glide-Hurst CK, Kishan AU, Fuller CD. Magnetic resonance linear accelerator technology and adaptive radiation therapy: An overview for clinicians. CA Cancer J Clin 2022; 72:34-56. [PMID: 34792808 PMCID: PMC8985054 DOI: 10.3322/caac.21707] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/01/2021] [Accepted: 09/22/2021] [Indexed: 12/25/2022] Open
Abstract
Radiation therapy (RT) continues to play an important role in the treatment of cancer. Adaptive RT (ART) is a novel method through which RT treatments are evolving. With the ART approach, computed tomography or magnetic resonance (MR) images are obtained as part of the treatment delivery process. This enables the adaptation of the irradiated volume to account for changes in organ and/or tumor position, movement, size, or shape that may occur over the course of treatment. The advantages and challenges of ART maybe somewhat abstract to oncologists and clinicians outside of the specialty of radiation oncology. ART is positioned to affect many different types of cancer. There is a wide spectrum of hypothesized benefits, from small toxicity improvements to meaningful gains in overall survival. The use and application of this novel technology should be understood by the oncologic community at large, such that it can be appropriately contextualized within the landscape of cancer therapies. Likewise, the need to test these advances is pressing. MR-guided ART (MRgART) is an emerging, extended modality of ART that expands upon and further advances the capabilities of ART. MRgART presents unique opportunities to iteratively improve adaptive image guidance. However, although the MRgART adaptive process advances ART to previously unattained levels, it can be more expensive, time-consuming, and complex. In this review, the authors present an overview for clinicians describing the process of ART and specifically MRgART.
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MESH Headings
- History, 20th Century
- History, 21st Century
- Humans
- Magnetic Resonance Imaging, Interventional/history
- Magnetic Resonance Imaging, Interventional/instrumentation
- Magnetic Resonance Imaging, Interventional/methods
- Magnetic Resonance Imaging, Interventional/trends
- Neoplasms/diagnostic imaging
- Neoplasms/radiotherapy
- Particle Accelerators
- Radiation Oncology/history
- Radiation Oncology/instrumentation
- Radiation Oncology/methods
- Radiation Oncology/trends
- Radiotherapy Planning, Computer-Assisted/history
- Radiotherapy Planning, Computer-Assisted/instrumentation
- Radiotherapy Planning, Computer-Assisted/methods
- Radiotherapy Planning, Computer-Assisted/trends
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Affiliation(s)
- William A. Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Beth Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Christopher Schultz
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Alison Tree
- The Royal Marsden National Health Service Foundation Trust and the Institute of Cancer Research, London, United Kingdom
| | - Musaddiq Awan
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Daniel A. Low
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, California
| | - Brigid A. McDonald
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Travis Salzillo
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | - Carri K. Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Amar U. Kishan
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, California
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas
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Di Dio C, Chiloiro G, Cusumano D, Catucci F, Boldrini L, Romano A, Meldolesi E, Marazzi F, Corvari B, Barbaro B, Manfredi R, Valentini V, Gambacorta MA. Fractal-Based Radiomic Approach to Tailor the Chemotherapy Treatment in Rectal Cancer: A Generating Hypothesis Study. Front Oncol 2021; 11:774413. [PMID: 34956893 PMCID: PMC8695680 DOI: 10.3389/fonc.2021.774413] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 11/18/2021] [Indexed: 12/15/2022] Open
Abstract
Introduction The aim of this study was to create a radiomic model able to calculate the probability of 5-year disease-free survival (5yDFS) when oxaliplatin (OXA) is or not administered in patients with locally advanced rectal cancer (LARC) and treated with neoadjuvant chemoradiotherapy (nCRT), allowing physicians to choose the best chemotherapy (CT) regimen. Methods LARC patients with cT3–4 cN0 or cT1–4 cN1–2 were treated according to an nCRT protocol that included concomitant CT schedules with or without OXA and radiotherapy dose of 55 Gy in 25 fractions. Radiomic analysis was performed on the T2-weighted (T2-w) MR images acquired during the initial tumor staging. Statistical analysis was performed separately for the cohort of patients treated with and without OXA. The ability of every single radiomic feature in predicting 5yDFS as a univariate analysis was assessed using the Wilcoxon–Mann–Whitney (WMW) test or t-test. Two logistic models (one for each cohort) were calculated, and their performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). Results A total of 176 image features belonging to four families (morphological, statistical, textural, and fractal) were calculated for each patient. At the univariate analysis, the only feature showing significance in predicting 5yDFS was the maximum fractal dimension of the subpopulation identified considering 30% and 50% as threshold levels (maxFD30–50). Once the models were developed using this feature, an AUC of 0.67 (0.57–0.77) and 0.75 (0.56–0.95) was obtained for patients treated with and without OXA, respectively. A maxFD30–50 >1.6 was correlated to a higher 5yDFS probability in patients treated with OXA. Conclusion This study suggests that radiomic analysis of MR T2-w images can be used to define the optimal concomitant CT regimen for stage III LARC cancer patients. In particular, by providing an indication of the gross tumor volume (GTV) spatial heterogeneity at initial staging, maxFD30–50 seems to be able to predict the probability of 5yDFS. New studies including a larger cohort of patients and external validation sets are recommended to verify the results of this hypothesis-generating study.
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Affiliation(s)
- Carmela Di Dio
- UOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy
| | - Giuditta Chiloiro
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Davide Cusumano
- UOC Radioterapia Oncologica, Mater Olbia Hospital, Olbia, Italy.,Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | - Luca Boldrini
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Angela Romano
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Elisa Meldolesi
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Fabio Marazzi
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Barbara Corvari
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Brunella Barbaro
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Riccardo Manfredi
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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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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Chiloiro G, Cusumano D, de Franco P, Lenkowicz J, Boldrini L, Carano D, Barbaro B, Corvari B, Dinapoli N, Giraffa M, Meldolesi E, Manfredi R, Valentini V, Gambacorta MA. Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development. Radiol Med 2021; 127:11-20. [PMID: 34725772 DOI: 10.1007/s11547-021-01421-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 10/14/2021] [Indexed: 12/23/2022]
Abstract
PURPOSE Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). MATERIALS AND METHODS This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC). RESULTS The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (p = 0.6) between yMTB and CM. CONCLUSION Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.
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Affiliation(s)
- Giuditta Chiloiro
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Paola de Franco
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy.
| | - Jacopo Lenkowicz
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Davide Carano
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Brunella Barbaro
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Barbara Corvari
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Nicola Dinapoli
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Martina Giraffa
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Elisa Meldolesi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Riccardo Manfredi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Maria Antonietta Gambacorta
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
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Xue C, Yuan J, Poon DM, Zhou Y, Yang B, Yu SK, Cheung YK. Reliability of MRI radiomics features in MR-guided radiotherapy for prostate cancer: Repeatability, reproducibility, and within-subject agreement. Med Phys 2021; 48:6976-6986. [PMID: 34562286 DOI: 10.1002/mp.15232] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 08/27/2021] [Accepted: 09/03/2021] [Indexed: 01/06/2023] Open
Abstract
PURPOSE The MR-guided radiotherapy (MRgRT) images on the integrated MRI and linear accelerator (MR-LINAC) might facilitate radiomics analysis for longitudinal treatment response assessment. However, the reliability of MRgRT radiomics features is largely unknown. This study aims to investigate MRgRT radiomics feature reliability acquired using a standardized 3D-T2W-TSE sequence in terms of repeatability, reproducibility, and within-subject feature agreement on a 1.5T MR-simulator and a 1.5T MR-LINAC for prostate cancer (PC). METHODS Twenty-six consecutive PC patients who underwent one MRI-simulator scan and two MR-LINAC scans before dose delivery were retrospectively included. The three MRI datasets were rigidly co-registered. 1023 first-order and texture radiomics features were extracted with different intensity bin widths for each scan in the manually segmented clinical target volume (CTV) and planning target volume (PTV) by an experienced radiation oncologist. Intraclass correlation coefficient (ICC) was used to evaluate feature repeatability between MR-LINAC scans and reproducibility between MRI-simulator and MR-LINAC scans. The within-subject feature value agreements were evaluated using Bland-Altman analysis. The impact of inter-observer segmentation on the radiomics feature reliability was also examined based on the second manual segmentation of CTV and PTV by an MRI researcher. RESULTS Based on the segmentation by the radiation oncologist and the default bin width of 25, 9.6%, 24.1%, 49.6%, and 16.8% of the total 1023 features exhibited excellent (ICC > 0.9), good (0.9 > ICC > 0.75), moderate (0.75 > ICC > 0.5), and poor (ICC < 0.5) repeatability in the CTV, and 9.2%, 26.8%, 50.5%, and 13.5% in the PTV, respectively. For reproducibility, the corresponding feature percentages were 8.9%, 19.7%, 41.9%, and 29.6% in the CTV, and 8.4%, 17.8%, 47.9%, and 26% in the PTV. Feature reliability was not notably influenced by intensity bin width for discretization. BA analysis revealed wide 95% limit-of-agreements and substantial biases of feature values between CTV and PTV and between any two MRI scans. The features even with excellent ICC were still subjected to considerable inter-scan feature variations in each individual subject. The analysis on the second segmentation by the MRI researcher showed insignificantly different feature repeatability and reproducibility in terms of ICC values. CONCLUSIONS Only a small proportion of features exhibited excellent/good repeatability and reproducibility, highlighting the importance of reliable MRgRT feature selection. The within-subject feature values were subjected to considerable inter-scan variations, imposing a challenge on the determination of the smallest detectable change in future MRgRT delta-radiomics studies.
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Affiliation(s)
- Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong, SAR, China
| | - Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong, SAR, China
| | - Darren Mc Poon
- Comprehensive Oncology Center, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong, SAR, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong, SAR, China
| | - Bin Yang
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong, SAR, China
| | - Siu Ki Yu
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong, SAR, China
| | - Yin Kin Cheung
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong, SAR, China
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25
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New developments in MRI: System characterization, technical advances and radiotherapy applications. Phys Med 2021; 90:50-52. [PMID: 34537500 DOI: 10.1016/j.ejmp.2021.09.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 09/03/2021] [Indexed: 11/20/2022] Open
Abstract
A Special Issue of Physica Medica - European Journal of Medical Physics, focused on some important points of contact between the world of magnetic resonance and that of medical physics, was published during 2021. This Editorial describes and comments on the content of this Focus Issue, which contains articles from leading groups invited by the Guest Editors.
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A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity. Cancers (Basel) 2021; 13:cancers13153835. [PMID: 34359737 PMCID: PMC8345157 DOI: 10.3390/cancers13153835] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Dosiomics is born directly as an extension of radiomics: it entails extracting features from the patients’ three-dimensional (3D) radiotherapy dose distribution rather than from conventional medical images to obtain specific spatial and statistical information. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. This study provides the first multicentre evaluation of the dosiomic features in terms of reproducibility, stability and sensitivity across various dose distributions obtained from multiple technologies and techniques and considering different dose calculation algorithms of TPS and two different resolutions of the dose grid. Harmonisation strategies to account for a possible variation in the dose distribution due to these confounding factors should be adopted when investigating a correlation between dosiomic features and clinical outcomes in multicentre studies. Abstract Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.
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Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers (Basel) 2021; 13:cancers13143590. [PMID: 34298803 PMCID: PMC8303203 DOI: 10.3390/cancers13143590] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary This review based on a literature search aims at showing the impact of Texture Analysis in the prediction of response to neoadjuvant radiotherapy and/or chemoradiotherapy. The manuscript explores radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma and rectal cancer in order to shed a light in the setting of neoadjuvant radiotherapy that can be used to tailor the best subsequent therapeutical strategy. Abstract Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Luca Boldrini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Davide Franceschini
- Radiotherapy and Radiosurgery Department, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Milan, Italy;
| | - Ilaria Morelli
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
- Correspondence: ; Tel.: +39-055-7947719
| | - Carlotta Becherini
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
| | - Mauro Loi
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Daniela Greto
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
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Cusumano D, Boldrini L, Dhont J, Fiorino C, Green O, Güngör G, Jornet N, Klüter S, Landry G, Mattiucci GC, Placidi L, Reynaert N, Ruggieri R, Tanadini-Lang S, Thorwarth D, Yadav P, Yang Y, Valentini V, Verellen D, Indovina L. Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives. Phys Med 2021; 85:175-191. [PMID: 34022660 DOI: 10.1016/j.ejmp.2021.05.010] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/15/2021] [Accepted: 05/04/2021] [Indexed: 12/14/2022] Open
Abstract
Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition. Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch. Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI. This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist's perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.
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Affiliation(s)
- Davide Cusumano
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | | | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | - Olga Green
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Görkem Güngör
- Acıbadem MAA University, School of Medicine, Department of Radiation Oncology, Maslak Istanbul, Turkey
| | - Núria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Spain
| | - Sebastian Klüter
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Munich, Germany
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
| | - Nick Reynaert
- Department of Medical Physics, Institut Jules Bordet, Belgium
| | - Ruggero Ruggieri
- Dipartimento di Radioterapia Oncologica Avanzata, IRCCS "Sacro cuore - don Calabria", Negrar di Valpolicella (VR), Italy
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tüebingen, Tübingen, Germany
| | - Poonam Yadav
- Department of Human Oncology School of Medicine and Public Heath University of Wisconsin - Madison, USA
| | - Yingli Yang
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, USA
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Dirk Verellen
- Department of Medical Physics, Iridium Cancer Network, Belgium; Faculty of Medicine and Health Sciences, Antwerp University, Antwerp, Belgium
| | - Luca Indovina
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
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