1
|
Morelli L, Paganelli C, Marvaso G, Parrella G, Annunziata S, Vicini MG, Zaffaroni M, Pepa M, Summers PE, De Cobelli O, Petralia G, Jereczek-Fossa BA, Baroni G. Addressing intra- and inter-institution variability of a radiomic framework based on Apparent Diffusion Coefficient in prostate cancer. Med Phys 2024. [PMID: 39172115 DOI: 10.1002/mp.17355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 06/27/2024] [Accepted: 08/05/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND Prostate cancer (PCa) is a highly heterogeneous disease, making tailored treatment approaches challenging. Magnetic resonance imaging (MRI), notably diffusion-weighted imaging (DWI) and the derived Apparent Diffusion Coefficient (ADC) maps, plays a crucial role in PCa characterization. In this context, radiomics is a very promising approach able to disclose insights from MRI data. However, the sensitivity of radiomic features to MRI settings, encompassing DWI protocols and multicenter variations, requires the development of robust and generalizable models. PURPOSE To develop a comprehensive radiomics framework for noninvasive PCa characterization using ADC maps, focusing on identifying reliable imaging biomarkers against intra- and inter-institution variations. MATERIALS AND METHODS Two patient cohorts, including an internal cohort (118 PCa patients) used for both training (75%) and hold-out testing (25%), and an external cohort (50 PCa patients) for independent testing, were employed in the study. DWI images were acquired with three different DWI protocols on two different MRI scanners: two DWI protocols acquired on a 1.5-T scanner for the internal cohort, and one DWI protocol acquired on a 3-T scanner for the external cohort. One hundred and seven radiomics features (i.e., shape, first order, texture) were extracted from ADC maps of the whole prostate gland. To address variations in DWI protocols and multicenter variability, a dedicated pipeline, including two-way ANOVA, sequential-feature-selection (SFS), and ComBat features harmonization was implemented. Mann-Whitney U-tests (α = 0.05) were performed to find statistically significant features dividing patients with different tumor characteristics in terms of Gleason score (GS) and T-stage. Support-Vector-Machine models were then developed to predict GS and T-stage, and the performance was assessed through the area under the curve (AUC) of receiver-operating-characteristic curves. RESULTS Downstream of ANOVA, two subsets of 38 and 41 features stable against DWI protocol were identified for GS and T-stage, respectively. Among these, SFS revealed the most predictive features, yielding an AUC of 0.75 (GS) and 0.70 (T-stage) in the hold-out test. Employing ComBat harmonization improved the external-test performance of the GS model, raising AUC from 0.72 to 0.78. CONCLUSION By incorporating stable features with a harmonization procedure and validating the model on an external dataset, model robustness, and generalizability were assessed, highlighting the potential of ADC and radiomics for PCa characterization.
Collapse
Affiliation(s)
- Letizia Morelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Giulia Marvaso
- Department of Radiation Oncology, European Institute of Oncology (IEO), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giovanni Parrella
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Simone Annunziata
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Maria Giulia Vicini
- Department of Radiation Oncology, European Institute of Oncology (IEO), Milan, Italy
| | - Mattia Zaffaroni
- Department of Radiation Oncology, European Institute of Oncology (IEO), Milan, Italy
| | - Matteo Pepa
- Department of Radiation Oncology, European Institute of Oncology (IEO), Milan, Italy
| | | | - Ottavio De Cobelli
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Urology, European Institute of Oncology (IEO), Milan, Italy
| | - Giuseppe Petralia
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Radiology, European Institute of Oncology (IEO), Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Department of Radiation Oncology, European Institute of Oncology (IEO), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| |
Collapse
|
2
|
Trojani V, Bassi MC, Verzellesi L, Bertolini M. Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review. Cancers (Basel) 2024; 16:2668. [PMID: 39123396 PMCID: PMC11311340 DOI: 10.3390/cancers16152668] [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: 06/21/2024] [Revised: 07/16/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. MATERIALS AND METHODS We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters' influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. RESULTS After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models' best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. CONCLUSIONS From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.
Collapse
Affiliation(s)
- Valeria Trojani
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
| | | | - Laura Verzellesi
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
| | - Marco Bertolini
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
| |
Collapse
|
3
|
Zhang Y, Liu H, Chang C, Yin Y, Wang R. Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics. PLoS One 2024; 19:e0300170. [PMID: 38568892 PMCID: PMC10990193 DOI: 10.1371/journal.pone.0300170] [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: 10/31/2023] [Accepted: 02/22/2024] [Indexed: 04/05/2024] Open
Abstract
Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based radiomics model. It aims to distinguish between the histological subtypes of lung adenocarcinoma and squamous cell carcinoma, employing four different machine learning techniques. A total of 255 Non-Small Cell Lung Cancer (NSCLC) patients were retrospectively analyzed and randomly divided into the training (n = 177) and validation (n = 78) sets, respectively. Radiomics features were extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was employed for feature selection. Subsequently, models were constructed using four distinct machine learning techniques, with the top-performing algorithm determined by evaluating metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC). The efficacy of the various models was appraised and compared using the DeLong test. A nomogram was developed based on the model with the best predictive efficiency and clinical utility, and it was validated using calibration curves. Results indicated that the logistic regression classifier had better predictive power in the validation cohort of the radiomic model. The combined model (AUC 0.870) exhibited superior predictive power compared to the clinical model (AUC 0.848) and the radiomics model (AUC 0.774). In this study, we discovered that the combined model, refined by the logistic regression classifier, exhibited the most effective performance in classifying the histological subtypes of NSCLC.
Collapse
Affiliation(s)
- Yalin Zhang
- Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China
- Xinjiang Key Laboratory of Oncology, Urumqi, China
| | - Huiling Liu
- Department of Radiation Oncology, Binzhou People’s Hospital, Binzhou, China
| | - Cheng Chang
- Department of Nuclear Medicine, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ruozheng Wang
- Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China
- Xinjiang Key Laboratory of Oncology, Urumqi, China
| |
Collapse
|
4
|
van der Reijd DJ, Chupetlovska K, van Dijk E, Westerink B, Monraats MA, Van Griethuysen JJM, Lambregts DMJ, Tissier R, Beets-Tan RGH, Benson S, Maas M. Multi-sequence MRI radiomics of colorectal liver metastases: Which features are reproducible across readers? Eur J Radiol 2024; 172:111346. [PMID: 38309217 DOI: 10.1016/j.ejrad.2024.111346] [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: 11/17/2023] [Revised: 01/15/2024] [Accepted: 01/25/2024] [Indexed: 02/05/2024]
Abstract
PURPOSE To assess the inter-reader reproducibility of radiomics features on multiple MRI sequences after segmentations of colorectal liver metastases (CRLM). METHOD 30 CRLM (in 23 patients) were manually delineated by three readers on MRI before the start of chemotherapy on the contrast enhanced T1-weighted images (CE-T1W) in the portal venous phase, T2-weighted images (T2W) and b800 diffusion weighted images (DWI). DWI delineations were copied to the ADC-maps. 107 radiomics features were extracted per sequence. The intraclass correlation coefficient (ICC) was calculated per feature. Features were considered reproducible if ICC > 0.9. RESULTS 90% of CE-T1W features were reproducible with a median ICC of 0.98 (range 0.76-1.00). 81% of DWI features were robust with median ICC = 0.97 (range 0.38-1.00). The T2W features had a median ICC of 0.96 (range 0.55-0.99) and were reproducible in 80%. ADC showed the lowest number of reproducible features with 58% and median ICC = 0.91 (range 0.38-0.99) When considering the lower bound of the ICC 95% confidence intervals, 58%, 66%, 54% and 29% reached 0.9 for the CE-T1W, DWI, T2W and ADC features, respectively. The feature class with the best reproducibility differed per sequence. CONCLUSIONS The majority of MRI radiomics features from CE-T1W, T2W, DWI and ADC in colorectal liver metastases were robust for segmentation variability between readers. The CE-T1W yielded slightly better reproducibility results compared to DWI and T2W. The ADC features seem more susceptible to reader differences compared to the other three sequences.
Collapse
Affiliation(s)
- Denise J van der Reijd
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands
| | - Kalina Chupetlovska
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Eleanor van Dijk
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Bram Westerink
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Melanie A Monraats
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Joost J M Van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands
| | - Renaud Tissier
- Biostatistics Center, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands; Faculty of Health Sciences, University of Southern Denmark, Campusvej 55, DK 5203 Odense, Denmark
| | - Sean Benson
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; Department of Cardiology, Amsterdam University Medical Centres, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands.
| |
Collapse
|
5
|
El Homsi M, Bane O, Fauveau V, Hectors S, Vietti Violi N, Sylla P, Ko HB, Cuevas J, Carbonell G, Nehlsen A, Vanguri R, Viswanath S, Jambawalikar S, Shaish H, Taouli B. Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics. Abdom Radiol (NY) 2024; 49:791-800. [PMID: 38150143 DOI: 10.1007/s00261-023-04128-0] [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: 07/06/2023] [Revised: 11/06/2023] [Accepted: 11/12/2023] [Indexed: 12/28/2023]
Abstract
PURPOSE To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT). METHODS This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set. RESULTS Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59-0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%]. CONCLUSION Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results.
Collapse
Affiliation(s)
- Maria El Homsi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA.
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York av, New York, USA.
| | - Octavia Bane
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Valentin Fauveau
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stefanie Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Naik Vietti Violi
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Patricia Sylla
- Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Huai-Bin Ko
- Department of Pathology, Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pathology, Columbia University Medical Center, New York, NY, USA
| | - Jordan Cuevas
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Guillermo Carbonell
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Virgen de la Arrixaca University Clinical Hospital, University of Murcia, Murcia, Spain
| | - Anthony Nehlsen
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rami Vanguri
- Department of Epidemiology & Biostatistics, Columbia University Medical Center, New York, NY, USA
| | - Satish Viswanath
- Department of Radiology, Case Western University, Cleveland, OH, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | - Hiram Shaish
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | - Bachir Taouli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
6
|
Knuth F, Tohidinezhad F, Winter RM, Bakke KM, Negård A, Holmedal SH, Ree AH, Meltzer S, Traverso A, Redalen KR. Quantitative MRI-based radiomics analysis identifies blood flow feature associated to overall survival for rectal cancer patients. Sci Rep 2024; 14:258. [PMID: 38167665 PMCID: PMC10762039 DOI: 10.1038/s41598-023-50966-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 12/26/2023] [Indexed: 01/05/2024] Open
Abstract
Radiomics objectively quantifies image information through numerical metrics known as features. In this study, we investigated the stability of magnetic resonance imaging (MRI)-based radiomics features in rectal cancer using both anatomical MRI and quantitative MRI (qMRI), when different methods to define the tumor volume were used. Second, we evaluated the prognostic value of stable features associated to 5-year progression-free survival (PFS) and overall survival (OS). On a 1.5 T MRI scanner, 81 patients underwent diagnostic MRI, an extended diffusion-weighted sequence with calculation of the apparent diffusion coefficient (ADC) and a multiecho dynamic contrast sequence generating both dynamic contrast-enhanced and dynamic susceptibility contrast (DSC) MR, allowing quantification of Ktrans, blood flow (BF) and area under the DSC curve (AUC). Radiomic features were extracted from T2w images and from ADC, Ktrans, BF and AUC maps. Tumor volumes were defined with three methods; machine learning, deep learning and manual delineations. The interclass correlation coefficient (ICC) assessed the stability of features. Internal validation was performed on 1000 bootstrap resamples in terms of discrimination, calibration and decisional benefit. For each combination of image and volume definition, 94 features were extracted. Features from qMRI contained higher prognostic potential than features from anatomical MRI. When stable features (> 90% ICC) were compared with clinical parameters, qMRI features demonstrated the best prognostic potential. A feature extracted from the DSC MRI parameter BF was associated with both PFS (p = 0.004) and OS (p = 0.004). In summary, stable qMRI-based radiomics features was identified, in particular, a feature based on BF from DSC MRI was associated with both PFS and OS.
Collapse
Affiliation(s)
- Franziska Knuth
- Department of Physics, Norwegian University of Science and Technology, Høgskoleringen 5, 7491, Trondheim, Norway
| | - Fariba Tohidinezhad
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, The Netherlands
| | - René M Winter
- Department of Physics, Norwegian University of Science and Technology, Høgskoleringen 5, 7491, Trondheim, Norway
| | - Kine Mari Bakke
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anne Negård
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Radiology, Akershus University Hospital, Lørenskog, Norway
| | - Stein H Holmedal
- Department of Radiology, Akershus University Hospital, Lørenskog, Norway
| | - Anne Hansen Ree
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sebastian Meltzer
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Kathrine Røe Redalen
- Department of Physics, Norwegian University of Science and Technology, Høgskoleringen 5, 7491, Trondheim, Norway.
| |
Collapse
|
7
|
Miranda J, Horvat N, Araujo-Filho JAB, Albuquerque KS, Charbel C, Trindade BMC, Cardoso DL, de Padua Gomes de Farias L, Chakraborty J, Nomura CH. The Role of Radiomics in Rectal Cancer. J Gastrointest Cancer 2023; 54:1158-1180. [PMID: 37155130 PMCID: PMC11301614 DOI: 10.1007/s12029-022-00909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/26/2022] [Indexed: 05/10/2023]
Abstract
PURPOSE Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT. METHODS We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically. RESULTS The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice. CONCLUSION Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.
Collapse
Affiliation(s)
- Joao Miranda
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
| | - Jose A B Araujo-Filho
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | - Kamila S Albuquerque
- Department of Radiology, Hospital Beneficência Portuguesa, 637 Maestro Cardim, Sao Paulo, SP, 01323-001, Brazil
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Bruno M C Trindade
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
| | - Daniel L Cardoso
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | | | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
| |
Collapse
|
8
|
Schurink NW, van Kranen SR, van Griethuysen JJM, Roberti S, Snaebjornsson P, Bakers FCH, de Bie SH, Bosma GPT, Cappendijk VC, Geenen RWF, Neijenhuis PA, Peterson GM, Veeken CJ, Vliegen RFA, Peters FP, Bogveradze N, El Khababi N, Lahaye MJ, Maas M, Beets GL, Beets-Tan RGH, Lambregts DMJ. Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer. Eur Radiol 2023; 33:8889-8898. [PMID: 37452176 PMCID: PMC10667134 DOI: 10.1007/s00330-023-09920-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
OBJECTIVES To develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset. METHODS Baseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1-2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups: (1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval (2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation (3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length (4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC) Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97). RESULTS After external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48-0.72) to predict complete response and 0.65 (95%CI=0.53-0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables. CONCLUSIONS Overall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset). CLINICAL RELEVANCE STATEMENT Predicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization. KEY POINTS This multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer. Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates. No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.
Collapse
Affiliation(s)
- Niels W Schurink
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Simon R van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Sander Roberti
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Petur Snaebjornsson
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Frans C H Bakers
- Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Shira H de Bie
- Department of Radiology, Deventer Ziekenhuis, Schalkhaar, The Netherlands
| | - Gerlof P T Bosma
- Department of Interventional Radiology, Elisabeth Tweesteden Hospital, Tilburg, The Netherlands
| | - Vincent C Cappendijk
- Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - Remy W F Geenen
- Department of Radiology, Northwest Clinics, Alkmaar, The Netherlands
| | | | | | - Cornelis J Veeken
- Department of Radiology, IJsselland Hospital, Capelle aan den IJssel, The Netherlands
| | - Roy F A Vliegen
- Department of Radiology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Femke P Peters
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Radiology, Acad. F. Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Najim El Khababi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Max J Lahaye
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Geerard L Beets
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Surgery, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Institute of Regional Health Research, University of Southern Denmark, Vejle, Denmark
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands.
| |
Collapse
|
9
|
Amintas S, Giraud N, Fernandez B, Dupin C, Denost Q, Garant A, Frulio N, Smith D, Rullier A, Rullier E, Vuong T, Dabernat S, Vendrely V. The Crying Need for a Better Response Assessment in Rectal Cancer. Curr Treat Options Oncol 2023; 24:1507-1523. [PMID: 37702885 PMCID: PMC10643426 DOI: 10.1007/s11864-023-01125-9] [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: 07/09/2023] [Indexed: 09/14/2023]
Abstract
OPINION STATEMENT Since total neoadjuvant treatment achieves almost 30% pathologic complete response, organ preservation has been increasingly debated for good responders after neoadjuvant treatment for patients diagnosed with rectal cancer. Two organ preservation strategies are available: a watch and wait strategy and a local excision strategy including patients with a near clinical complete response. A major issue is the selection of patients according to the initial tumor staging or the response assessment. Despite modern imaging improvement, identifying complete response remains challenging. A better selection could be possible by radiomics analyses, exploiting numerous image features to feed data characterization algorithms. The subsequent step is to include baseline and/or pre-therapeutic MRI, PET-CT, and CT radiomics added to the patients' clinicopathological data, inside machine learning (ML) prediction models, with predictive or prognostic purposes. These models could be further improved by the addition of new biomarkers such as circulating tumor biomarkers, molecular profiling, or pathological immune biomarkers.
Collapse
Affiliation(s)
- Samuel Amintas
- Tumor Biology and Tumor Bank Laboratory, CHU Bordeaux, F-33600, Pessac, France.
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France.
| | - Nicolas Giraud
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| | | | - Charles Dupin
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| | - Quentin Denost
- Bordeaux Colorectal Institute, F-33000, Bordeaux, France
| | - Aurelie Garant
- UT Southwestern Department of Radiation Oncology, Dallas, USA
| | - Nora Frulio
- Radiology Department, CHU Bordeaux, F-33600, Pessac, France
| | - Denis Smith
- Department of Digestive Oncology, CHU Bordeaux, F-33600, Pessac, France
| | - Anne Rullier
- Histology Department, CHU Bordeaux, F-33000, Bordeaux, France
| | - Eric Rullier
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Surgery Department, CHU Bordeaux, F-33600, Pessac, France
| | - Te Vuong
- Department of Radiation Oncology, McGill University, Jewish General Hospital, Montreal, Canada
| | - Sandrine Dabernat
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Biochemistry Department, CHU Bordeaux, F-33000, Bordeaux, France
| | - Véronique Vendrely
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| |
Collapse
|
10
|
Sherminie LPG, Jayatilake ML, Hewavithana B, Weerakoon BS, Vijithananda SM. Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy. Front Oncol 2023; 13:1139902. [PMID: 37664038 PMCID: PMC10470056 DOI: 10.3389/fonc.2023.1139902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 07/31/2023] [Indexed: 09/05/2023] Open
Abstract
Introduction Gliomas are still considered as challenging in oncologic management despite the developments in treatment approaches. The complete elimination of a glioma might not be possible even after a treatment and assessment of therapeutic response is important to determine the future course of actions for patients with such cancers. In the recent years radiomics has emerged as a promising solution with potential applications including prediction of therapeutic response. Hence, this study was focused on investigating whether morphometry-based radiomics signature could be used to predict therapeutic response in patients with gliomas following radiotherapy. Methods 105 magnetic resonance (MR) images including segmented and non-segmented images were used to extract morphometric features and develop a morphometry-based radiomics signature. After determining the appropriate machine learning algorithm, a prediction model was developed to predict the therapeutic response eliminating the highly correlated features as well as without eliminating the highly correlated features. Then the model performance was evaluated. Results Tumor grade had the highest contribution to develop the morphometry-based signature. Random forest provided the highest accuracy to train the prediction model derived from the morphometry-based radiomics signature. An accuracy of 86% and area under the curve (AUC) value of 0.91 were achieved for the prediction model evaluated without eliminating the highly correlated features whereas accuracy and AUC value were 84% and 0.92 respectively for the prediction model evaluated after eliminating the highly correlated features. Discussion Nonetheless, the developed morphometry-based radiomics signature could be utilized as a noninvasive biomarker for therapeutic response in patients with gliomas following radiotherapy.
Collapse
Affiliation(s)
- Lahanda Purage G. Sherminie
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Mohan L. Jayatilake
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Badra Hewavithana
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| | - Bimali S. Weerakoon
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Sahan M. Vijithananda
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| |
Collapse
|
11
|
Parvaze PS, Bhattacharjee R, Verma YK, Singh RK, Yadav V, Singh A, Khanna G, Ahlawat S, Trivedi R, Patir R, Vaishya S, Shah TJ, Gupta RK. Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis. NMR IN BIOMEDICINE 2023; 36:e4884. [PMID: 36453877 DOI: 10.1002/nbm.4884] [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: 06/22/2022] [Revised: 11/11/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex vivo experimentation of radiomics analysis of T1-weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on radiomics features. This retrospective study involved magnetic resonance (MR) images acquired using a 3.0-T MR machine from 83 patients with 48 GB, 21 BM, and 14 meningioma. The 93 radiomics features were extracted from each subject's PVE mask from three pathologies using T1-dynamic contrast-enhanced MR imaging. Statistically significant (< 0.05, independent samples T-test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1-weighted cell line images but group comparison was carried out using one-way analysis of variance. Further, a random forest (RF)-based machine learning model was designed to classify the PVE of GB and BM. Texture-based variations, especially higher nonuniformity values, were observed in the PVE of GB. No significance was observed between BM and meningioma PVE. In cell line images, the culture medium had higher nonuniformity and was considerably reduced with increasing cell densities in four features. The RF model implemented with highly significant features provided improved area under the curve results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in the differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with a larger cohort and across different scanners in the future.
Collapse
Affiliation(s)
| | - Rupsa Bhattacharjee
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Yogesh Kumar Verma
- Stem Cell & Gene Therapy Research Group, Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, Delhi, India
| | - Rakesh Kumar Singh
- Department of Radiology and Imaging, Fortis Memorial, Research Institute, Gurugram, India
| | - Virendra Yadav
- Medical Image and Signal Processing Lab, CBME, Indian Institute of Technology, Delhi, India
| | - Anup Singh
- Medical Image and Signal Processing Lab, CBME, Indian Institute of Technology, Delhi, India
| | - Gaurav Khanna
- SRL Diagnostics, Fortis Memorial Research Institute, Gurugram, India
| | - Sunita Ahlawat
- SRL Diagnostics, Fortis Memorial Research Institute, Gurugram, India
| | - Richa Trivedi
- NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, Delhi, India
| | - Rana Patir
- Department of Neurosurgery, Fortis Memorial Research Institute, Gurugram, India
| | - Sandeep Vaishya
- Department of Neurosurgery, Fortis Memorial Research Institute, Gurugram, India
| | | | - Rakesh K Gupta
- Department of Radiology and Imaging, Fortis Memorial, Research Institute, Gurugram, India
| |
Collapse
|
12
|
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.
Collapse
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.
| |
Collapse
|
13
|
Scalco E, Rizzo G, Mastropietro A. The quantification of IntraVoxel incoherent motion - MRI maps cannot preserve texture information: An evaluation based on simulated and in-vivo images. Comput Biol Med 2023; 154:106495. [PMID: 36669333 DOI: 10.1016/j.compbiomed.2022.106495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/15/2022] [Accepted: 12/27/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Radiomics can be applied on parametric maps obtained from IntraVoxel Incoherent Motion (IVIM) MRI to characterize heterogeneity in diffusion and perfusion tissue properties. The purpose of this work is to assess the accuracy and reproducibility of radiomic features computed from IVIM maps using different fitting methods. METHODS 200 digitally simulated IVIM-MRI images with various SNR containing different combinations of texture patterns were generated from ground truth maps of true diffusion D, pseudo-diffusion D* and perfusion fraction f. Four different methods (segmented least-square LSQ, Bayesian, supervised and unsupervised deep learning DL) were adopted to quantify IVIM maps from simulations and from two real images of liver tumor. Radiomic features were computed from ground truth and estimated maps. Accuracy and reproducibility among quantification methods were assessed. RESULTS Almost 50% of radiomic features computed from D maps using DL approaches, 36% using Bayes and 27% using LSQ presented errors lower than 50%. Radiomic features from f and D* maps were accurate only if computed using DL methods from histogram. High reproducibility (ICC>0.8) was found only for D maps among DL and Bayes methods, whereas features from f and D* maps were less reproducible, with LSQ approach in lower agreement with the others. CONCLUSIONS Texture patterns were preserved and correctly estimated only on D maps, except for LSQ approach. We suggest limiting radiomic analysis only to histogram and some texture features from D maps, to histogram features from f maps, and to avoid it on D* maps.
Collapse
Affiliation(s)
- Elisa Scalco
- Institute for Biomedical Technologies, National Research Council (ITB-CNR), Segrate, MI, Italy.
| | - Giovanna Rizzo
- Institute for Biomedical Technologies, National Research Council (ITB-CNR), Segrate, MI, Italy
| | - Alfonso Mastropietro
- Institute for Biomedical Technologies, National Research Council (ITB-CNR), Segrate, MI, Italy
| |
Collapse
|
14
|
Wichtmann BD, Harder FN, Weiss K, Schönberg SO, Attenberger UI, Alkadhi H, Pinto Dos Santos D, Baeßler B. Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging. Invest Radiol 2023; 58:199-208. [PMID: 36070524 DOI: 10.1097/rli.0000000000000921] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Before implementing radiomics in routine clinical practice, comprehensive knowledge about the repeatability and reproducibility of radiomic features is required. The aim of this study was to systematically investigate the influence of image processing parameters on radiomic features from magnetic resonance imaging (MRI) in terms of feature values as well as test-retest repeatability. MATERIALS AND METHODS Utilizing a phantom consisting of 4 onions, 4 limes, 4 kiwifruits, and 4 apples, we acquired a test-retest dataset featuring 3 of the most commonly used MRI sequences on a 3 T scanner, namely, a T1-weighted, a T2-weighted, and a fluid-attenuated inversion recovery sequence, each at high and low resolution. After semiautomatic image segmentation, image processing with systematic variation of image processing parameters was performed, including spatial resampling, intensity discretization, and intensity rescaling. For each respective image processing setting, a total of 45 radiomic features were extracted, corresponding to the following 7 matrices/feature classes: conventional indices, histogram matrix, shape matrix, gray-level zone length matrix, gray-level run length matrix, neighboring gray-level dependence matrix, and gray-level cooccurrence matrix. Systematic differences of individual features between different resampling steps were assessed using 1-way analysis of variance with Tukey-type post hoc comparisons to adjust for multiple testing. Test-retest repeatability of radiomic features was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient. RESULTS Image processing influenced radiological feature values. Regardless of the acquired sequence and feature class, significant differences ( P < 0.05) in feature values were found when the size of the resampled voxels was too large, that is, bigger than 3 mm. Almost all higher-order features depended strongly on intensity discretization. The effects of intensity rescaling were negligible except for some features derived from T1-weighted sequences. For all sequences, the percentage of repeatable features (concordance correlation coefficient and dynamic range ≥ 0.9) varied considerably depending on the image processing settings. The optimal image processing setting to achieve the highest percentage of stable features varied per sequence. Irrespective of image processing, the fluid-attenuated inversion recovery sequence in high-resolution overall yielded the highest number of stable features in comparison with the other sequences (89% vs 64%-78% for the respective optimal image processing settings). Across all sequences, the most repeatable features were generally obtained for a spatial resampling close to the originally acquired voxel size and an intensity discretization to at least 32 bins. CONCLUSION Variation of image processing parameters has a significant impact on the values of radiomic features as well as their repeatability. Furthermore, the optimal image processing parameters differ for each MRI sequence. Therefore, it is recommended that these processing parameters be determined in corresponding test-retest scans before clinical application. Extensive repeatability, reproducibility, and validation studies as well as standardization are required before quantitative image analysis and radiomics can be reliably translated into routine clinical care.
Collapse
Affiliation(s)
- Barbara D Wichtmann
- From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Felix N Harder
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | | | - Stefan O Schönberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Germany
| | - Ulrike I Attenberger
- From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | | | | |
Collapse
|
15
|
Liu H, Zhao D, Huang Y, Li C, Dong Z, Tian H, Sun Y, Lu Y, Chen C, Wu H, Zhang Y. Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma. Front Oncol 2023; 13:1129918. [PMID: 37025592 PMCID: PMC10072214 DOI: 10.3389/fonc.2023.1129918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/13/2023] [Indexed: 04/08/2023] Open
Abstract
Purpose To propose and evaluate a comprehensive modeling approach combing radiomics, dosiomics and clinical components, for more accurate prediction of locoregional recurrence risk after radiotherapy for patients with locoregionally advanced HPSCC. Materials and methods Clinical data of 77 HPSCC patients were retrospectively investigated, whose median follow-up duration was 23.27 (4.83-81.40) months. From the planning CT and dose distribution, 1321 radiomics and dosiomics features were extracted respectively from planning gross tumor volume (PGTV) region each patient. After stability test, feature dimension was further reduced by Principal Component Analysis (PCA), yielding Radiomic and Dosiomic Principal Components (RPCs and DPCs) respectively. Multiple Cox regression models were constructed using various combinations of RPC, DPC and clinical variables as the predictors. Akaike information criterion (AIC) and C-index were used to evaluate the performance of Cox regression models. Results PCA was performed on 338 radiomic and 873 dosiomic features that were tested as stable (ICC1 > 0.7 and ICC2 > 0.95), yielding 5 RPCs and DPCs respectively. Three comprehensive features (RPC0, P<0.01, DPC0, P<0.01 and DPC3, P<0.05) were found to be significant in the individual Radiomic or Dosiomic Cox regression models. The model combining the above features and clinical variable (total stage IVB) provided best risk stratification of locoregional recurrence (C-index, 0.815; 95%CI, 0.770-0.859) and prevailing balance between predictive accuracy and complexity (AIC, 143.65) than any other investigated models using either single factors or two combined components. Conclusion This study provided quantitative tools and additional evidence for the personalized treatment selection and protocol optimization for HPSCC, a relatively rare cancer. By combining complementary information from radiomics, dosiomics, and clinical variables, the proposed comprehensive model provided more accurate prediction of locoregional recurrence risk after radiotherapy.
Collapse
Affiliation(s)
- Hongjia Liu
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Dan Zhao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yuliang Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Chenguang Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Zhengkun Dong
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Hongbo Tian
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yijie Sun
- School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Yanye Lu
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Chen Chen
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China
| | - Hao Wu
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yibao Zhang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
- *Correspondence: Yibao Zhang,
| |
Collapse
|
16
|
Ibrahim A, Lu L, Yang H, Akin O, Schwartz LH, Zhao B. The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model. APPLIED SCIENCES (BASEL, SWITZERLAND) 2022; 12:9824. [PMID: 37091743 PMCID: PMC10121203 DOI: 10.3390/app12199824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.
Collapse
Affiliation(s)
- Abdalla Ibrahim
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Correspondence:
| | - Lin Lu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Hao Yang
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lawrence H. Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| |
Collapse
|
17
|
Horvat N, Miranda J, El Homsi M, Peoples JJ, Long NM, Simpson AL, Do RKG. A primer on texture analysis in abdominal radiology. Abdom Radiol (NY) 2022; 47:2972-2985. [PMID: 34825946 DOI: 10.1007/s00261-021-03359-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 01/18/2023]
Abstract
The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.
Collapse
Affiliation(s)
- Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil
| | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Niamh M Long
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Amber L Simpson
- School of Computing, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
| |
Collapse
|
18
|
Cui Y, Yin FF. Impact of image quality on radiomics applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7fd7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.
Collapse
|
19
|
Thrussell I, Winfield JM, Orton MR, Miah AB, Zaidi SH, Arthur A, Thway K, Strauss DC, Collins DJ, Koh DM, Oelfke U, Huang PH, O’Connor JPB, Messiou C, Blackledge MD. Radiomic Features From Diffusion-Weighted MRI of Retroperitoneal Soft-Tissue Sarcomas Are Repeatable and Exhibit Change After Radiotherapy. Front Oncol 2022; 12:899180. [PMID: 35924167 PMCID: PMC9343063 DOI: 10.3389/fonc.2022.899180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
Background Size-based assessments are inaccurate indicators of tumor response in soft-tissue sarcoma (STS), motivating the requirement for new response imaging biomarkers for this rare and heterogeneous disease. In this study, we assess the test-retest repeatability of radiomic features from MR diffusion-weighted imaging (DWI) and derived maps of apparent diffusion coefficient (ADC) in retroperitoneal STS and compare baseline repeatability with changes in radiomic features following radiotherapy (RT). Materials and Methods Thirty patients with retroperitoneal STS received an MR examination prior to treatment, of whom 23/30 were investigated in our repeatability analysis having received repeat baseline examinations and 14/30 patients were investigated in our post-treatment analysis having received an MR examination after completing pre-operative RT. One hundred and seven radiomic features were extracted from the full manually delineated tumor region using PyRadiomics. Test-retest repeatability was assessed using an intraclass correlation coefficient (baseline ICC), and post-radiotherapy variance analysis (post-RT-IMS) was used to compare the change in radiomic feature value to baseline repeatability. Results For the ADC maps and DWI images, 101 and 102 features demonstrated good baseline repeatability (baseline ICC > 0.85), respectively. Forty-three and 2 features demonstrated both good baseline repeatability and a high post-RT-IMS (>0.85), respectively. Pearson correlation between the baseline ICC and post-RT-IMS was weak (0.432 and 0.133, respectively). Conclusions The ADC-based radiomic analysis shows better test-retest repeatability compared with features derived from DWI images in STS, and some of these features are sensitive to post-treatment change. However, good repeatability at baseline does not imply sensitivity to post-treatment change.
Collapse
Affiliation(s)
- Imogen Thrussell
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Jessica M. Winfield
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Matthew R. Orton
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Aisha B. Miah
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Shane H. Zaidi
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Amani Arthur
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Khin Thway
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- Department of Histopathology, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Dirk C. Strauss
- Department of Surgery, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - David J. Collins
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Dow-Mu Koh
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Uwe Oelfke
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Paul H. Huang
- Division of Molecular Pathology, The Institute of Cancer Research, London, United Kingdom
| | - James P. B. O’Connor
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
- Department of Radiology, The Christie Hospital, Manchester, United Kingdom
| | - Christina Messiou
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Matthew D. Blackledge
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| |
Collapse
|
20
|
Rai R, Barton MB, Chlap P, Liney G, Brink C, Vinod S, Heinke M, Trada Y, Holloway LC. Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer. J Med Imaging (Bellingham) 2022; 9:044005. [PMID: 35992729 PMCID: PMC9386367 DOI: 10.1117/1.jmi.9.4.044005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 08/09/2022] [Indexed: 08/20/2023] Open
Abstract
Purpose: Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. Approach: An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers. Results: We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. Conclusions: The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.
Collapse
Affiliation(s)
- Robba Rai
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Michael B. Barton
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Phillip Chlap
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Gary Liney
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Carsten Brink
- Odense University Hospital, Laboratory of Radiation Physics, Department of Oncology, Odense, Denmark
- University of Southern Denmark, Department of Clinical Research, Odense, Denmark
| | - Shalini Vinod
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | | | - Yuvnik Trada
- Calvary Mater Newcastle, Department of Radiation Oncology, Newcastle, New South Wales, Australia
| | - Lois C. Holloway
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- University of Wollongong, Centre of Radiation Physics, Wollongong, New South Wales, Australia
- University of Sydney, Institute of Medical Physics, School of Physics, Sydney, New South Wales, Australia
| |
Collapse
|
21
|
Jiménez de los Santos ME, Reyes-Pérez JA, Domínguez Osorio V, Villaseñor-Navarro Y, Moreno-Astudillo L, Vela-Sarmiento I, Sollozo-Dupont I. Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer. World J Gastroenterol 2022; 28:2609-2624. [PMID: 35949349 PMCID: PMC9254137 DOI: 10.3748/wjg.v28.i23.2609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/25/2021] [Accepted: 04/25/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Whole-tumor apparent diffusion coefficient (ADC) histogram analysis is relevant to predicting the neoadjuvant chemoradiation therapy (nCRT) response in patients with locally advanced rectal cancer (LARC).
AIM To evaluate the performance of ADC histogram-derived parameters for predicting the outcomes of patients with LARC.
METHODS This is a single-center, retrospective study, which included 48 patients with LARC. All patients underwent a pre-treatment magnetic resonance imaging (MRI) scan for primary tumor staging and a second restaging MRI for response evaluation. The sample was distributed as follows: 18 responder patients (R) and 30 non-responders (non-R). Eight parameters derived from the whole-lesion histogram analysis (ADCmean, skewness, kurtosis, and ADC10th, 25th, 50th, 75th, 90th percentiles), as well as the ADCmean from the hot spot region of interest (ROI), were calculated for each patient before and after treatment. Then all data were compared between R and non-R using the Mann-Whitney U test. Two measures of diagnostic accuracy were applied: the receiver operating characteristic curve and the diagnostic odds ratio (DOR). We also reported intra- and interobserver variability by calculating the intraclass correlation coefficient (ICC).
RESULTS Post-nCRT kurtosis, as well as post-nCRT skewness, were significantly lower in R than in non-R (both P < 0.001, respectively). We also found that, after treatment, R had a larger loss of both kurtosis and skewness than non-R (∆%kurtosis and ∆skewness, P < 0.001). Other parameters that demonstrated changes between groups were post-nCRT ADC10th, ∆%ADC10th, ∆%ADCmean, and ROI ∆%ADCmean. However, the best diagnostic performance was achieved by ∆%kurtosis at a threshold of 11.85% (Area under the receiver operating characteristic curve [AUC] = 0.991, DOR = 376), followed by post-nCRT kurtosis = 0.78 × 10-3 mm2/s (AUC = 0.985, DOR = 375.3), ∆skewness = 0.16 (AUC = 0.885, DOR = 192.2) and post-nCRT skewness = 1.59 × 10-3 mm2/s (AUC = 0.815, DOR = 168.6). Finally, intraclass correlation coefficient analysis showed excellent intraobserver and interobserver agreement, ensuring the implementation of histogram analysis into routine clinical practice.
CONCLUSION Whole-tumor ADC histogram parameters, particularly kurtosis and skewness, are relevant biomarkers for predicting the nCRT response in LARC. Both parameters appear to be more reliable than ADCmean from one-slice ROI.
Collapse
Affiliation(s)
| | | | | | | | | | - Itzel Vela-Sarmiento
- Department of Gastrointestinal Surgery, National Cancer Institute, Mexico 14080, Mexico
| | | |
Collapse
|
22
|
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.
Collapse
|
23
|
Marfisi D, Tessa C, Marzi C, Del Meglio J, Linsalata S, Borgheresi R, Lilli A, Lazzarini R, Salvatori L, Vignali C, Barucci A, Mascalchi M, Casolo G, Diciotti S, Traino AC, Giannelli M. Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy. Sci Rep 2022; 12:10186. [PMID: 35715531 PMCID: PMC9205876 DOI: 10.1038/s41598-022-13937-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 03/21/2022] [Indexed: 12/24/2022] Open
Abstract
Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study was to investigate, for the first time, the effect of image resampling/discretization and filtering on radiomic features estimation from quantitative CMR T1 and T2 mapping. Specifically, T1 and T2 maps of 26 patients with hypertrophic cardiomyopathy (HCM) were used to estimate 98 radiomic features for 7 different resampling voxel sizes (at fixed bin width), 9 different bin widths (at fixed resampling voxel size), and 7 different spatial filters (at fixed resampling voxel size/bin width). While we found a remarkable dependence of myocardial radiomic features from T1 and T2 mapping on image filters, many radiomic features showed a limited sensitivity to resampling voxel size/bin width, in terms of intraclass correlation coefficient (> 0.75) and coefficient of variation (< 30%). The estimate of most textural radiomic features showed a linear significant (p < 0.05) correlation with resampling voxel size/bin width. Overall, radiomic features from T2 maps have proven to be less sensitive to image preprocessing than those from T1 maps, especially when varying bin width. Our results might corroborate the potential of radiomics from T1/T2 mapping in HCM and hopefully in other myocardial diseases.
Collapse
Affiliation(s)
- Daniela Marfisi
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Carlo Tessa
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Apuane Hospital, 54100, Massa, Italy
| | - Chiara Marzi
- Institute of Applied Physics "Nello Carrara", Italian National Research Council, 50019, Sesto Fiorentino, Italy
| | - Jacopo Del Meglio
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Stefania Linsalata
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Alessio Lilli
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Riccardo Lazzarini
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Luca Salvatori
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Claudio Vignali
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Andrea Barucci
- Institute of Applied Physics "Nello Carrara", Italian National Research Council, 50019, Sesto Fiorentino, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50121, Florence, Italy
| | - Giancarlo Casolo
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 47522, Cesena, Italy
| | - Antonio Claudio Traino
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy.
| |
Collapse
|
24
|
Scalco E, Rizzo G, Mastropietro A. The stability of oncologic MRI radiomic features and the potential role of deep learning: a review. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac60b9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/24/2022] [Indexed: 11/11/2022]
Abstract
Abstract
The use of MRI radiomic models for the diagnosis, prognosis and treatment response prediction of tumors has been increasingly reported in literature. However, its widespread adoption in clinics is hampered by issues related to features stability. In the MRI radiomic workflow, the main factors that affect radiomic features computation can be found in the image acquisition and reconstruction phase, in the image pre-processing steps, and in the segmentation of the region of interest on which radiomic indices are extracted. Deep Neural Networks (DNNs), having shown their potentiality in the medical image processing and analysis field, can be seen as an attractive strategy to partially overcome the issues related to radiomic stability and mitigate their impact. In fact, DNN approaches can be prospectively integrated in the MRI radiomic workflow to improve image quality, obtain accurate and reproducible segmentations and generate standardized images. In this review, DNN methods that can be included in the image processing steps of the radiomic workflow are described and discussed, in the light of a detailed analysis of the literature in the context of MRI radiomic reliability.
Collapse
|
25
|
Boldrini L, Lenkowicz J, Orlandini LC, Yin G, Cusumano D, Chiloiro G, Dinapoli N, Peng Q, Casà C, Gambacorta MA, Valentini V, Lang J. Applicability of a pathological complete response magnetic resonance-based radiomics model for locally advanced rectal cancer in intercontinental cohort. Radiat Oncol 2022; 17:78. [PMID: 35428267 PMCID: PMC9013126 DOI: 10.1186/s13014-022-02048-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/04/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Predicting pathological complete response (pCR) in patients affected by locally advanced rectal cancer (LARC) who undergo neoadjuvant chemoradiotherapy (nCRT) is a challenging field of investigation, but many of the published models are burdened by a lack of reliable external validation. Aim of this study was to evaluate the applicability of a magnetic resonance imaging (MRI) radiomic-based pCR model developed and validated in Europe, to a different cohort of patients from an intercontinental cancer center. METHODS The original model was based on two clinical and two radiomics features extracted from T2-weighted 1.5 T MRI of 161 LARC patients acquired before nCRT, considered as training set. Such model is here validated using the T2-w 1.5 and 3 T staging MRI of 59 LARC patients with different clinical characteristics consecutively treated in mainland Chinese cancer center from March 2017 to January 2018. Model performance were evaluated in terms of area under the receiver operator characteristics curve (AUC) and relative parameters, such as accuracy, specificity, negative and positive predictive value (NPV and PPV). RESULTS An AUC of 0.83 (CI 95%, 0.71-0.96) was achieved for the intercontinental cohort versus a value of 0.75 (CI 95%, 0.61-0.88) at the external validation step reported in the original experience. Considering the best cut-off threshold identified in the first experience (0.26), the following predictive performance were obtained: 0.65 as accuracy, 0.64 as specificity, 0.70 as sensitivity, 0.91 as NPV and 0.28 as PPV. CONCLUSIONS Despite the introduction of significant different factors, the proposed model appeared to be replicable on a real-world data extra-European patients' cohort, achieving a TRIPOD 4 level.
Collapse
Affiliation(s)
- Luca Boldrini
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Jacopo Lenkowicz
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Lucia Clara Orlandini
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Gang Yin
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Davide Cusumano
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Giuditta Chiloiro
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Nicola Dinapoli
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Qian Peng
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Calogero Casà
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Vincenzo Valentini
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Jinyi Lang
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
26
|
Abunahel BM, Pontre B, Ko J, Petrov MS. Towards developing a robust radiomics signature in diffuse diseases of the pancreas: Accuracy and stability of features derived from T1-weighted magnetic resonance imaging. J Med Imaging Radiat Sci 2022; 53:420-428. [DOI: 10.1016/j.jmir.2022.04.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 12/16/2022]
|
27
|
Linsalata S, Borgheresi R, Marfisi D, Barca P, Sainato A, Paiar F, Neri E, Traino AC, Giannelli M. Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2003286. [PMID: 35355820 PMCID: PMC8958068 DOI: 10.1155/2022/2003286] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 01/14/2022] [Accepted: 01/30/2022] [Indexed: 12/24/2022]
Abstract
The purpose of this study was to investigate the effect of image preprocessing on radiomic features estimation from computed tomography (CT) imaging of locally advanced rectal cancer (LARC). CT images of 20 patients with LARC were used to estimate 105 radiomic features of 7 classes (shape, first-order, GLCM, GLDM, GLRLM, GLSZM, and NGTDM). Radiomic features were estimated for 6 different isotropic resampling voxel sizes, using 10 interpolation algorithms (at fixed bin width) and 6 different bin widths (at fixed interpolation algorithm). The intraclass correlation coefficient (ICC) and the coefficient of variation (CV) were calculated to assess the variability in radiomic features estimation due to preprocessing. A repeated measures correlation analysis was performed to assess any linear correlation between radiomic feature estimate and resampling voxel size or bin width. Reproducibility of radiomic feature estimate, when assessed through ICC analysis, was nominally excellent (ICC > 0.9) for shape features, good (0.75 < ICC ≤ 0.9) or moderate (0.5 < ICC ≤ 0.75) for first-order features, and moderate or poor (0 ≤ ICC ≤ 0.5) for textural features. A number of radiomic features characterized by good or excellent reproducibility in terms of ICC showed however median CV values greater than 15%. For most textural features, a significant (p < 0.05) correlation between their estimate and resampling voxel size or bin width was found. In CT imaging of patients with LARC, the estimate of textural features, as well as of first-order features to a lesser extent, is appreciably biased by preprocessing. Accordingly, this should be taken into account when planning clinical or research studies, as well as when comparing results from different studies and performing multicenter studies.
Collapse
Affiliation(s)
- Stefania Linsalata
- Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
| | - Rita Borgheresi
- Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
| | - Daniela Marfisi
- Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
| | - Patrizio Barca
- Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
| | - Aldo Sainato
- Radiation Oncology Unit, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
| | - Fabiola Paiar
- Radiation Oncology Unit, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
| | - Emanuele Neri
- Diagnostic and Interventional Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Antonio Claudio Traino
- Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
| |
Collapse
|
28
|
Boca (Petresc) B, Caraiani C, Popa L, Lebovici A, Feier DS, Bodale C, Buruian MM. The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study. BIOLOGY 2022; 11:biology11030452. [PMID: 35336825 PMCID: PMC8945327 DOI: 10.3390/biology11030452] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/04/2022] [Accepted: 03/14/2022] [Indexed: 11/16/2022]
Abstract
Simple Summary Metachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases. Abstract This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.
Collapse
Affiliation(s)
- Bianca Boca (Petresc)
- Department of Radiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (B.B.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania; (A.L.); (D.S.F.)
- Department of Medical Imaging, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Cosmin Caraiani
- Department of Medical Imaging, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Radiology, Regional Institute of Gastroenterology and Hepatology “Prof. Dr. Octavian Fodor”, 400158 Cluj-Napoca, Romania
- Correspondence: (C.C.); (L.P.)
| | - Loredana Popa
- Department of Medical Imaging, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Correspondence: (C.C.); (L.P.)
| | - Andrei Lebovici
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania; (A.L.); (D.S.F.)
- Department of Radiology, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Diana Sorina Feier
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania; (A.L.); (D.S.F.)
- Department of Radiology, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Carmen Bodale
- Department of Oncology, Amethyst Radiotherapy Center Cluj, 407280 Florești, Romania;
- Department of Medical Oncology and Radiotherapy, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Mircea Marian Buruian
- Department of Radiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (B.B.); (M.M.B.)
| |
Collapse
|
29
|
Schurink NW, van Kranen SR, Roberti S, van Griethuysen JJM, Bogveradze N, Castagnoli F, El Khababi N, Bakers FCH, de Bie SH, Bosma GPT, Cappendijk VC, Geenen RWF, Neijenhuis PA, Peterson GM, Veeken CJ, Vliegen RFA, Beets-Tan RGH, Lambregts DMJ. Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility. Eur Radiol 2022; 32:1506-1516. [PMID: 34655313 PMCID: PMC8831294 DOI: 10.1007/s00330-021-08251-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/23/2021] [Accepted: 08/06/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software. METHODS T2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient. RESULTS Image features differed significantly (p < 0.001) between centers with more substantial variations in ADC compared to T2W-MRI. In total, 64.3% of the variation in mean ADC was explained by differences in hardware and acquisition, compared to 0.4% by patient-intrinsic factors. Feature reproducibility between expert and non-expert segmentations was good to excellent (median ICC 0.89-0.90). Reproducibility for single-slice versus whole-volume segmentations was substantially poorer (median ICC 0.40-0.58). Between software packages, reproducibility was good to excellent (median ICC 0.99) for most features (first-order/shape/GLCM/GLRLM) but poor for higher-order (GLSZM/NGTDM) features (median ICC 0.00-0.41). CONCLUSIONS Significant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models. KEY POINTS • Features derived from T2W-MRI and in particular ADC differ significantly between centers when performing multicenter data analysis. • Variations in ADC are mainly (> 60%) caused by hardware and image acquisition differences and less so (< 1%) by patient- or tumor-intrinsic variations. • Features derived using different image segmentations (expert/non-expert) were reproducible, provided that whole-volume segmentations were used. When using different feature extraction software packages with similar settings, higher-order features were less reproducible.
Collapse
Affiliation(s)
- Niels W Schurink
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Simon R van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Sander Roberti
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Radiology, Acad. F. Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Francesca Castagnoli
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
| | - Najim El Khababi
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Frans C H Bakers
- Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Shira H de Bie
- Department of Radiology, Deventer Ziekenhuis, Deventer, The Netherlands
| | - Gerlof P T Bosma
- Department of Interventional Radiology, Elisabeth Tweesteden Hospital, Tilburg, The Netherlands
| | - Vincent C Cappendijk
- Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - Remy W F Geenen
- Department of Radiology, Northwest Clinics, Alkmaar, The Netherlands
| | | | | | - Cornelis J Veeken
- Department of Radiology, IJsselland Hospital, Capelle Aan Den IJssel, The Netherlands
| | - Roy F A Vliegen
- Department of Radiology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands.
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands.
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands.
| |
Collapse
|
30
|
Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study. Tomography 2021; 7:866-876. [PMID: 34941645 PMCID: PMC8706942 DOI: 10.3390/tomography7040073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/20/2021] [Accepted: 11/29/2021] [Indexed: 11/17/2022] Open
Abstract
We aimed to evaluate the stability of radiomic features in the liver of healthy individuals across different three-dimensional regions of interest (3D ROI) sizes in T1-weighted (T1w) and T2-weighted (T2w) images from different MR scanners. We retrospectively included 66 examinations of patients without known diseases or pathological imaging findings acquired on three MRI scanners (3 Tesla I: 25 patients, 3 Tesla II: 19 patients, 1.5 Tesla: 22 patients). 3D ROIs of different diameters (10, 20, 30 mm) were drawn on T1w GRE and T2w TSE images into the liver parenchyma (segment V–VIII). We extracted 93 radiomic features from the different ROIs and tested features for significant differences with the Mann–Whitney-U (MWU)-test. The MWU-test revealed significant differences for most second- and higher-order features, indicating a systematic difference dependent on the ROI size. The features mean, median, root mean squared (RMS), 10th percentile, and 90th percentile were not significantly different. We also assessed feature robustness to ROI size variation with overall concordance correlation coefficients (OCCCs). OCCCs across the different ROI-sizes for mean, median, and RMS were excellent (>0.90) in both sequences on all three scanners. These features, therefore, seem robust to ROI-size variation and suitable for radiomic studies of liver MRI.
Collapse
|
31
|
Pfaehler E, Zhovannik I, Wei L, Boellaard R, Dekker A, Monshouwer R, El Naqa I, Bussink J, Gillies R, Wee L, Traverso A. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol 2021; 20:69-75. [PMID: 34816024 PMCID: PMC8591412 DOI: 10.1016/j.phro.2021.10.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 12/12/2022] Open
Abstract
Main factors impacting feature stability: Image acquisition, reconstruction, tumor segmentation, and interpolation. Textural features are less robust than morphological or statistical features. A checklist is provided including items that should be reported in a radiomic study.
Purpose Although quantitative image biomarkers (radiomics) show promising value for cancer diagnosis, prognosis, and treatment assessment, these biomarkers still lack reproducibility. In this systematic review, we aimed to assess the progress in radiomics reproducibility and repeatability in the recent years. Methods and materials Four hundred fifty-one abstracts were retrieved according to the original PubMed search pattern with the publication dates ranging from 2017/05/01 to 2020/12/01. Each abstract including the keywords was independently screened by four observers. Forty-two full-text articles were selected for further analysis. Patient population data, radiomic feature classes, feature extraction software, image preprocessing, and reproducibility results were extracted from each article. To support the community with a standardized reporting strategy, we propose a specific reporting checklist to evaluate the feasibility to reproduce each study. Results Many studies continue to under-report essential reproducibility information: all but one clinical and all but two phantom studies missed to report at least one important item reporting image acquisition. The studies included in this review indicate that all radiomic features are sensitive to image acquisition, reconstruction, tumor segmentation, and interpolation. However, the amount of sensitivity is feature dependent, for instance, textural features were, in general, less robust than statistical features. Conclusions Radiomics repeatability, reproducibility, and reporting quality can substantially be improved regarding feature extraction software and settings, image preprocessing and acquisition, cutoff values for stable feature selection. Our proposed radiomics reporting checklist can serve to simplify and improve the reporting and, eventually, guarantee the possibility to fully replicate and validate radiomic studies.
Collapse
Affiliation(s)
- Elisabeth Pfaehler
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ivan Zhovannik
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - René Monshouwer
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Jan Bussink
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert Gillies
- Department of Radiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| |
Collapse
|
32
|
Jensen LJ, Kim D, Elgeti T, Steffen IG, Hamm B, Nagel SN. Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study. ACTA ACUST UNITED AC 2021; 7:238-252. [PMID: 34201012 PMCID: PMC8293351 DOI: 10.3390/tomography7020022] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/19/2021] [Accepted: 06/03/2021] [Indexed: 02/01/2023]
Abstract
We aimed to evaluate radiomic features' stability across different region of interest (ROI) sizes in CT and MR images. We chose a phantom with a homogenous internal structure so no differences for a feature extracted from ROIs of different sizes would be expected. For this, we scanned a plastic cup filled with sodium chloride solution ten times in CT and per MR sequence (T1-weighted-gradient-echo and T2-weighted-turbo-inversion-recovery-magnitude). We placed sphere-shaped ROIs of different diameters (4, 8, and 16 mm, and 4, 8, and 16 pixels) into the phantom's center. Features were extracted using PyRadiomics. We assessed feature stability across ROI sizes with overall concordance correlation coefficients (OCCCs). Differences were tested for significance with the Mann-Whitney U-test. Of 93 features, 87 T1w-derived, 87 TIRM-derived, and 70 CT-derived features were significantly different between ROI sizes. Among MR-derived features, OCCCs showed excellent (>0.90) agreement for mean, median, and root mean squared for ROI sizes between 4 and 16 mm and pixels. We further observed excellent agreement for 10th and 90th percentile in T1w and 10th percentile in T2w TIRM images. There was no excellent agreement among the OCCCs of CT-derived features. In summary, many features indicated significant differences and only few showed excellent agreement across varying ROI sizes, although we examined a homogenous phantom. Since we considered a small phantom in an experimental setting, further studies to investigate this size effect would be necessary for a generalization. Nevertheless, we believe knowledge about this effect is crucial in interpreting radiomics studies, as features that supposedly discriminate disease entities may only indicate a systematic difference in ROI size.
Collapse
|
33
|
Repeatability and reproducibility study of radiomic features on a phantom and human cohort. Sci Rep 2021; 11:2055. [PMID: 33479392 PMCID: PMC7820018 DOI: 10.1038/s41598-021-81526-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 01/05/2021] [Indexed: 12/14/2022] Open
Abstract
The repeatability and reproducibility of radiomic features extracted from CT scans need to be investigated to evaluate the temporal stability of imaging features with respect to a controlled scenario (test–retest), as well as their dependence on acquisition parameters such as slice thickness, or tube current. Only robust and stable features should be used in prognostication/prediction models to improve generalizability across multiple institutions. In this study, we investigated the repeatability and reproducibility of radiomic features with respect to three different scanners, variable slice thickness, tube current, and use of intravenous (IV) contrast medium, combining phantom studies and human subjects with non-small cell lung cancer. In all, half of the radiomic features showed good repeatability (ICC > 0.9) independent of scanner model. Within acquisition protocols, changes in slice thickness was associated with poorer reproducibility compared to the use of IV contrast. Broad feature classes exhibit different behaviors, with only few features appearing to be the most stable. 108 features presented both good repeatability and reproducibility in all the experiments, most of them being wavelet and Laplacian of Gaussian features.
Collapse
|
34
|
Abstract
Carrying out large multicenter studies is one of the key goals to be achieved towards a faster transfer of the radiomics approach in the clinical setting. This requires large-scale radiomics data analysis, hence the need for integrating radiomic features extracted from images acquired in different centers. This is challenging as radiomic features exhibit variable sensitivity to differences in scanner model, acquisition protocols and reconstruction settings, which is similar to the so-called 'batch-effects' in genomics studies. In this review we discuss existing methods to perform data integration with the aid of reducing the unwanted variation associated with batch effects. We also discuss the future potential role of deep learning methods in providing solutions for addressing radiomic multicentre studies.
Collapse
Affiliation(s)
- R Da-Ano
- LaTiM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - D Visvikis
- LaTiM, INSERM, UMR 1101, Univ Brest, Brest, France
- equally contributed
| | - M Hatt
- LaTiM, INSERM, UMR 1101, Univ Brest, Brest, France
- equally contributed
| |
Collapse
|
35
|
Stability of dosomics features extraction on grid resolution and algorithm for radiotherapy dose calculation. Phys Med 2020; 77:30-35. [DOI: 10.1016/j.ejmp.2020.07.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 06/29/2020] [Accepted: 07/16/2020] [Indexed: 12/16/2022] Open
|
36
|
MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability. Sci Rep 2020; 10:14163. [PMID: 32843663 PMCID: PMC7447771 DOI: 10.1038/s41598-020-70940-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 07/31/2020] [Indexed: 12/11/2022] Open
Abstract
Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness. Therefore, our study aim was to determine the robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI. A total of 129 histologically confirmed breast tumors were segmented manually in three dimensions on the first post-contrast T1-weighted MR exam by four observers: a dedicated breast radiologist, a resident, a Ph.D. candidate, and a medical student. Robust features were assessed using the intraclass correlation coefficient (ICC > 0.9). The inter-observer variability was evaluated by the volumetric Dice Similarity Coefficient (DSC). The mean DSC for all tumors was 0.81 (range 0.19–0.96), indicating a good spatial overlap of the segmentations based on observers of varying expertise. In total, 41.6% (552/1328) and 32.8% (273/833) of all RadiomiX and Pyradiomics features, respectively, were identified as robust and were independent of inter-observer manual segmentation variability.
Collapse
|
37
|
Palmisano A, Di Chiara A, Esposito A, Rancoita PMV, Fiorino C, Passoni P, Albarello L, Rosati R, Del Maschio A, De Cobelli F. MRI prediction of pathological response in locally advanced rectal cancer: when apparent diffusion coefficient radiomics meets conventional volumetry. Clin Radiol 2020; 75:798.e1-798.e11. [PMID: 32712007 DOI: 10.1016/j.crad.2020.06.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 06/17/2020] [Indexed: 12/16/2022]
Abstract
AIM To investigate the role of diffusion-weighted imaging (DWI), T2-weighted (W) imaging, and apparent diffusion coefficient (ADC) histogram analysis before, during, and after neoadjuvant chemoradiotherapy (CRT) in the prediction of pathological response in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS Magnetic resonance imaging (MRI) at 1.5 T was performed in 43 patients with LARC before, during, and after CRT. Tumour volume was measured on both T2-weighted (VT2W) and on DWI at b=1,000 images (Vb,1,000) at each time point, hence the tumour volume reduction rate (ΔVT2W and ΔVb,1,000) was calculated. Whole-lesion (three-dimensional [3D]) first-order texture analysis of the ADC map was performed. Imaging parameters were compared to the pathological tumour regression grade (TRG). The diagnostic performance of each parameter in the identification of complete responders (CR; TRG4), partial responders (PR; TRG3) and non-responders (NR; TRG0-2) was evaluated by multinomial regression analysis and receiver operating characteristics curves. RESULTS After surgery, 11 patients were CR, 22 PR, and 10 NR. Before CRT, predictions of CR resulted in an ADC value of the 75th percentile and median, with good accuracy (74% and 86%, respectively) and sensitivity (73% and 82%, respectively). During CRT, the best predictor of CR was ΔVT2W (-58.3%) with good accuracy (81%) and excellent sensitivity (91%). After CRT, the best predictors of CR were ΔVT2W (-82.8%) and ΔVb, 1,000 (-86.8%), with 84% accuracy in both cases and 82% and 91% sensitivity, respectively. CONCLUSIONS The median ADC value at pre-treatment MRI and ΔVT2W (from pre-to-during CRT MRI) may have a role in early and accurate prediction of response to treatment. Both ΔVT2W and ΔVb,1,000 (from pre-to-post CRT) can help in the identification of CR after CRT.
Collapse
Affiliation(s)
- A Palmisano
- Unit of Clinical Research in Radiology, Experimental Imaging Center, IRCCS Ospedale San Raffaele, Milano, Italy.
| | - A Di Chiara
- Unit of Clinical Research in Radiology, Experimental Imaging Center, IRCCS Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
| | - A Esposito
- Unit of Clinical Research in Radiology, Experimental Imaging Center, IRCCS Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
| | - P M V Rancoita
- University Centre of Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milan, Italy
| | - C Fiorino
- Medical Physics, San Raffaele Hospital, Milano, Italy
| | - P Passoni
- Unit of Radiotherapy, IRCCS Ospedale San Raffaele, Milano, Italy
| | - L Albarello
- Department of Pathology, IRCCS Ospedale San Raffaele, Milano, Italy
| | - R Rosati
- Vita-Salute San Raffaele University, Milano, Italy; Department of Gastrointestinal Surgery, San Raffaele Hospital, Milano, Italy
| | - A Del Maschio
- Unit of Clinical Research in Radiology, Experimental Imaging Center, IRCCS Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
| | - F De Cobelli
- Unit of Clinical Research in Radiology, Experimental Imaging Center, IRCCS Ospedale San Raffaele, Milano, Italy; Vita-Salute San Raffaele University, Milano, Italy
| |
Collapse
|
38
|
Lu H, Parra NA, Qi J, Gage K, Li Q, Fan S, Feuerlein S, Pow-Sang J, Gillies R, Choi JW, Balagurunathan Y. Repeatability of Quantitative Imaging Features in Prostate Magnetic Resonance Imaging. Front Oncol 2020; 10:551. [PMID: 32457827 PMCID: PMC7221156 DOI: 10.3389/fonc.2020.00551] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 03/27/2020] [Indexed: 01/31/2023] Open
Abstract
Background: Multiparametric magnetic resonance imaging (mpMRI) has emerged as a non-invasive modality to diagnose and monitor prostate cancer. Quantitative metrics on the regions of abnormality have shown to be useful descriptors to discriminate clinically significant cancers. In this study, we evaluate the reproducibility of quantitative imaging features using repeated mpMRI on the same patients. Methods: We retrospectively obtained the deidentified records of patients, who underwent two mpMRI scans within 2 weeks of the first baseline scan. The patient records were obtained as deidentified data (including imaging), obtained through the TCIA (The Cancer Imaging Archive) repository and analyzed in our institution with an institutional review board-approved Health Insurance Portability and Accountability Act-compliant retrospective study protocol. Indicated biopsied regions were used as a marker for our study radiologists to delineate the regions of interest. We extracted 307 quantitative features in each mpMRI modality [T2-weighted MR sequence image (T2w) and apparent diffusion coefficient (ADC) with b values of 0 and 1,400 mm/s2] across the two sequential scans. Concordance correlation coefficients (CCCs) were computed on the features extracted from sequential scans. Redundant features were removed by computing the coefficient of determination (R 2) among them and replaced with a feature that had the highest dynamic range within intercorrelated groups. Results: We have assessed the reproducibility of quantitative imaging features among sequential scans and found that habitat region characterization improves repeatability in ADC maps. There were 19 T2w features and two ADC features in radiologist drawn regions (native raw image), compared to 18 T2w and 15 ADC features in habitat regions (sphere), which were reproducible (CCC ≥0.65) and non-redundant (R 2 ≥ 0.99). We also found that z-transformation of the images prior to feature extraction reduced the number of reproducible features with no detrimental effect. Conclusion: We have shown that there are quantitative imaging features that are reproducible across sequential prostate mpMRI acquisition at a preset level of filters. We also found that a habitat approach improves feature repeatability in ADC. A validated set of reproducible image features in mpMRI will allow us to develop clinically useful disease risk stratification, enabling the possibility of using imaging as a surrogate to invasive biopsies.
Collapse
Affiliation(s)
- Hong Lu
- Department of Radiology, Tianjin Medical and Cancer Hospital, Tianjin, China
- Departments of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Nestor A. Parra
- Departments of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Jin Qi
- Departments of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Kenneth Gage
- Departments of Diagnostic Imaging, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Qian Li
- Department of Radiology, Tianjin Medical and Cancer Hospital, Tianjin, China
| | - Shuxuan Fan
- Department of Radiology, Tianjin Medical and Cancer Hospital, Tianjin, China
- Departments of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Sebastian Feuerlein
- Departments of Diagnostic Imaging, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Julio Pow-Sang
- Departments of Genitourinary Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Robert Gillies
- Departments of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
- Departments of Diagnostic Imaging, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Jung W. Choi
- Departments of Diagnostic Imaging, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Yoganand Balagurunathan
- Departments of Diagnostic Imaging, H. Lee Moffitt Cancer Center, Tampa, FL, United States
- Departments of Genitourinary Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
- Departments of Bioinformatics & Biostatistics, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| |
Collapse
|
39
|
Cattell R, Chen S, Huang C. Robustness of radiomic features in magnetic resonance imaging: review and a phantom study. Vis Comput Ind Biomed Art 2019; 2:19. [PMID: 32240418 PMCID: PMC7099536 DOI: 10.1186/s42492-019-0025-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 10/09/2019] [Indexed: 02/07/2023] Open
Abstract
Radiomic analysis has exponentially increased the amount of quantitative data that can be extracted from a single image. These imaging biomarkers can aid in the generation of prediction models aimed to further personalized medicine. However, the generalizability of the model is dependent on the robustness of these features. The purpose of this study is to review the current literature regarding robustness of radiomic features on magnetic resonance imaging. Additionally, a phantom study is performed to systematically evaluate the behavior of radiomic features under various conditions (signal to noise ratio, region of interest delineation, voxel size change and normalization methods) using intraclass correlation coefficients. The features extracted in this phantom study include first order, shape, gray level cooccurrence matrix and gray level run length matrix. Many features are found to be non-robust to changing parameters. Feature robustness assessment prior to feature selection, especially in the case of combining multi-institutional data, may be warranted. Further investigation is needed in this area of research.
Collapse
Affiliation(s)
- Renee Cattell
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Shenglan Chen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Chuan Huang
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA. .,Department of Radiology, Stony Brook Medicine, Stony Brook, NY, 11794, USA. .,Department of Psychiatry, Stony Brook Medicine, Stony Brook, NY, 11794, USA.
| |
Collapse
|
40
|
Schick U, Lucia F, Dissaux G, Visvikis D, Badic B, Masson I, Pradier O, Bourbonne V, Hatt M. MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology. Br J Radiol 2019; 92:20190105. [PMID: 31538516 DOI: 10.1259/bjr.20190105] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Personalized medicine aims at offering optimized treatment options and improved survival for cancer patients based on individual variability. The success of precision medicine depends on robust biomarkers. Recently, the requirement for improved non-biologic biomarkers that reflect tumor biology has emerged and there has been a growing interest in the automatic extraction of quantitative features from medical images, denoted as radiomics. Radiomics as a methodological approach can be applied to any image and most studies have focused on PET, CT, ultrasound, and MRI. Here, we aim to present an overview of the radiomics workflow as well as the major challenges with special emphasis on the use of multiparametric MRI datasets. We then reviewed recent studies on radiomics in the field of pelvic oncology including prostate, cervical, and colorectal cancer.
Collapse
Affiliation(s)
- Ulrike Schick
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - François Lucia
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Gurvan Dissaux
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - Dimitris Visvikis
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Bogdan Badic
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Department of General and Digestive Surgery, University Hospital, Brest, France
| | - Ingrid Masson
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Olivier Pradier
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - Vincent Bourbonne
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| |
Collapse
|