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Abenavoli EM, Linguanti F, Anichini M, Miele V, Mungai F, Palazzo M, Nassi L, Puccini B, Romano I, Sordi B, Sciagrà R, Simontacchi G, Vannucchi AM, Berti V. Texture analysis of 18F-FDG PET/CT and CECT: Prediction of refractoriness of Hodgkin lymphoma with mediastinal bulk involvement. Hematol Oncol 2024; 42:e3261. [PMID: 38454623 DOI: 10.1002/hon.3261] [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: 09/26/2023] [Revised: 01/18/2024] [Accepted: 02/22/2024] [Indexed: 03/09/2024]
Abstract
To recognize patients at high risk of refractory disease, the identification of novel prognostic parameters improving stratification of newly diagnosed Hodgkin Lymphoma (HL) is still needed. This study investigates the potential value of metabolic and texture features, extracted from baseline 18F-FDG Positron Emission Tomography/Computed Tomography (PET) and Contrast-Enhanced Computed Tomography scan (CECT), together with clinical data, in predicting first-line therapy refractoriness (R) of classical HL (cHL) with mediastinal bulk involvement. We reviewed 69 cHL patients who underwent staging PET and CECT. Lesion segmentation and texture parameter extraction were performed using the freeware software LIFEx 6.3. The prognostic significance of clinical and imaging features was evaluated in relation to the development of refractory disease. Receiver operating characteristic curve, Cox proportional hazard regression and Kaplan-Meier analyses were performed to examine the potential independent predictors and to evaluate their prognostic value. Among clinical characteristics, only stage according to the German Hodgkin Group (GHSG) classification system significantly differed between R and not-R. Among CECT variables, only parameters derived from second order matrices (gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) demonstrated significant prognostic power. Among PET variables, SUVmean, several variables derived from first (histograms, shape), and second order analyses (GLCM, GLRLM, NGLDM) exhibited significant predictive power. Such variables obtained accuracies greater than 70% at receiver operating characteristic analysis and their PFS curves resulted statistically significant in predicting refractoriness. At multivariate analysis, only HISTO_EntropyPET extracted from PET (HISTO_EntropyPET ) and GHSG stage resulted as significant independent predictors. Their combination identified 4 patient groups with significantly different PFS curves, with worst prognosis in patients with higher HISTO_EntropyPET values, regardless of the stage. Imaging radiomics may provide a reference for prognostic evaluation of patients with mediastinal bulky cHL. The best prognostic value in the prediction of R versus not-R disease was reached by combining HISTO_EntropyPET with GHSG stage.
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Affiliation(s)
- Elisabetta M Abenavoli
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
| | - Flavia Linguanti
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
| | - Matilde Anichini
- Department of Radiology, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Francesco Mungai
- Department of Radiology, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Marianna Palazzo
- Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Luca Nassi
- Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Benedetta Puccini
- Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Ilaria Romano
- Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Benedetta Sordi
- Hematology Department, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy
| | - Roberto Sciagrà
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
| | - Gabriele Simontacchi
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Alessandro M Vannucchi
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy
| | - Valentina Berti
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy
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Wang X, Zhao L, Wang S, Zhao X, Chen L, Sun X, Liu Y, Liu J, Sun S. Utility of contrast-enhanced MRI radiomics features combined with clinical indicators for predicting induction chemotherapy response in primary central nervous system lymphoma. J Neurooncol 2024; 166:451-460. [PMID: 38308802 DOI: 10.1007/s11060-023-04554-6] [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: 11/27/2023] [Accepted: 12/23/2023] [Indexed: 02/05/2024]
Abstract
PURPOSE To assess the utility of combining contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics features with clinical variables in predicting the response to induction chemotherapy (IC) for primary central nervous system lymphoma (PCNSL). METHODS A total of 131 patients with PCNSL (101 in the training set and 30 in the testing set) who had undergone contrast-enhanced MRI scans were retrospectively analyzed. Pyradiomics was utilized to extract radiomics features, and the clinical variables of the patients were gathered. Radiomics prediction models were developed using different combinations of feature selection methods and machine learning models, and the best combination was ultimately chosen. We screened clinical variables associated with treatment outcomes and developed clinical prediction models. The predictive performance of radiomics model, clinical model, and combined model, which integrates the best radiomics model and clinical characteristics, was independently assessed and compared using Receiver Operating Characteristic (ROC) curves. RESULTS In total, we extracted 1598 features. The best radiomics model we selected as the best utilized T-test and Recursive Feature Elimination (RFE) for feature selection and logistic regression for model building. Serum Interleukin 2 Receptor (IL-2R) and Eastern Cooperative Oncology Group (ECOG) Score were utilized to develop a clinical predictive model for assessing the response to induction chemotherapy. The results of the testing set revealed that the combined prediction model (radiomics and IL-2R) achieved the highest area under the ROC curve at 0.868 (0.683, 0.967), followed by the radiomics model at 0.857 (0.681, 0.957), and the clinical prediction model (IL-2R and ECOG) at 0.618 (0.413, 0.797). The combined model was significantly more accurate than the clinical model, with an AUC of 0.868 compared to 0.618 (P < 0.05). While the radiomics model had slightly better predictive power than the clinical model, this difference was not statistically significant (AUC, 0.857 vs. 0.618, P > 0.05). CONCLUSIONS Our prediction model, which combines radiomics signatures from CE-MRI with serum IL-2R, can effectively stratify patients with PCNSL before high-dose methotrexate (HD-MTX) -based chemotherapy.
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Affiliation(s)
- Xiaochen Wang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neuroradiology, Beijing Neurosurgical Institute, Beijing, China
| | - Litao Zhao
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Sihui Wang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuening Zhao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lingxu Chen
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xuefei Sun
- Department of Hematology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuanbo Liu
- Department of Hematology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiangang Liu
- School of Engineering Medicine, Beihang University, Beijing, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, China.
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, China.
| | - Shengjun Sun
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- Department of Neuroradiology, Beijing Neurosurgical Institute, Beijing, China.
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CT radiomics to predict Deauville score 4 positive and negative Hodgkin lymphoma manifestations. Sci Rep 2022; 12:20008. [PMID: 36411307 PMCID: PMC9678888 DOI: 10.1038/s41598-022-24227-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 11/11/2022] [Indexed: 11/23/2022] Open
Abstract
18F-FDG-PET/CT is standard to assess response in Hodgkin lymphoma by quantifying metabolic activity with the Deauville score. PET/CT, however, is time-consuming, cost-extensive, linked to high radiation and has a low availability. As an alternative, we investigated radiomics from non-contrast-enhanced computed tomography (NECT) scans. 75 PET/CT examinations of 43 patients on two different scanners were included. Target lesions were classified as Deauville score 4 positive (DS4+) or negative (DS4-) based on their SUVpeak and then segmented in NECT images. From these segmentations, 107 features were extracted with PyRadiomics. All further statistical analyses were then performed scanner-wise: differences between DS4+ and DS4- manifestations were assessed with the Mann-Whitney-U-test and single feature performances with the ROC-analysis. To further verify the reliability of the results, the number of features was reduced using different techniques. The feature median showed a high sensitivity for DS4+ manifestations on both scanners (scanner A: 0.91, scanner B: 0.85). It furthermore was the only feature that remained in both datasets after applying different feature reduction techniques. The feature median from NECT concordantly has a high sensitivity for DS4+ Hodgkin manifestations on two different scanners and thus could provide a surrogate for increased metabolic activity in PET/CT.
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Ungan G, Lavandier AF, Rouanet J, Hordonneau C, Chauveau B, Pereira B, Boyer L, Garcier JM, Mansard S, Bartoli A, Magnin B. Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification. Int J Comput Assist Radiol Surg 2022; 17:1867-1877. [PMID: 35650345 DOI: 10.1007/s11548-022-02662-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/26/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Immunotherapy has dramatically improved the prognosis of patients with metastatic melanoma (MM). Yet, there is a lack of biomarkers to predict whether a patient will benefit from immunotherapy. Our aim was to create radiomics models on pretreatment computed tomography (CT) to predict overall survival (OS) and treatment response in patients with MM treated with anti-PD-1 immunotherapy. METHODS We performed a monocentric retrospective analysis of 503 metastatic lesions in 71 patients with 46 radiomics features extracted following lesion segmentation. Predictive accuracies for OS < 1 year versus > 1 year and treatment response versus no response was compared for five feature selection methods (sequential forward selection, recursive, Boruta, relief, random forest) and four classifiers (support vector machine (SVM), random forest, K-nearest neighbor, logistic regression (LR)) used with or without SMOTE data augmentation. A fivefold cross-validation was performed at the patient level, with a tumour-based classification. RESULTS The highest accuracy level for OS predictions was obtained with 3D lesions (0.91) without clinical data integration when combining Boruta feature selection and the LR classifier, The highest accuracy for treatment response prediction was obtained with 3D lesions (0.88) without clinical data integration when combining Boruta feature selection, the LR classifier and SMOTE data augmentation. The accuracy was significantly higher concerning OS prediction with 3D segmentation (0.91 vs 0.86) while clinical data integration led to improved accuracy notably in 2D lesions (0.76 vs 0.87) regarding treatment response prediction. Skewness was the only feature found to be an independent predictor of OS (HR (CI 95%) 1.34, p-value 0.001). CONCLUSION This is the first study to investigate CT texture parameter selection and classification methods for predicting MM prognosis with treatment by immunotherapy. Combining pretreatment CT radiomics features from a single tumor with data selection and classifiers may accurately predict OS and treatment response in MM treated with anti-PD-1.
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Affiliation(s)
- Gulnur Ungan
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Anne-Flore Lavandier
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Jacques Rouanet
- Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Constance Hordonneau
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Benoit Chauveau
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Bruno Pereira
- Biostatistics Unit, DRCI, CHU Clermont Ferrand, 58 rue Montalembert, 63000, Clermont-Ferrand, France
| | - Louis Boyer
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Jean-Marc Garcier
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France.,Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Sandrine Mansard
- Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Adrien Bartoli
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Benoit Magnin
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France. .,Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France. .,Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
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Doillon M, Durot C, Pluchart C, Marcus C, Djelouah M, Carsin-Vu A. Response to Induction Therapy in Pediatric Hodgkin's Lymphoma: Performance of First-Order Texture Parameters of CT Images. J Belg Soc Radiol 2022; 106:46. [PMID: 35647484 PMCID: PMC9104423 DOI: 10.5334/jbsr.2752] [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: 01/04/2022] [Accepted: 04/22/2022] [Indexed: 11/20/2022] Open
Abstract
Objectives The aim of this study was to examine whether texture analysis features on pretreatment contrast-enhanced CT images could predict adequate response (AR) or inadequate response (IR) after two cycles of chemotherapy in pediatric Hodgkin's lymphoma (PHL). Materials and methods This retrospective single-center study included 32 children and adolescents with HL. Texture analysis was independently performed by two radiologists using pretreatment CT scans. The mean gray level, standard deviation, entropy, kurtosis, and skewness were derived from pixel distribution histograms before and after spatial filtration ranging from two (fine texture) to six (coarse texture). Interobserver reliability was studied using interobserver correlation coefficients (ICCs) to select texture parameters. Relationships between early response assessment (ERA) to induction therapy and associated factors were studied using Student's t-tests and a lasso penalized logistic regression analysis. Results Of the 32 patients, IR was observed in 13 and AR in 19. Inter-reader agreement was good to excellent (ICC > 0.75) for all parameters except skewness and kurtosis without filtration and at spatial scale filtration (SSF) = 2. These parameters were excluded from the analysis. The t-test identified only entropy at SSF = 2 (p value = 0.039) as a potential predictor of ERA. No parameters were significantly associated with ERA, according to a lasso penalized logistic regression. Conclusion No textural parameters were identified as predictors of ERA after two cycles of chemotherapy in PHL.
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Affiliation(s)
- Margaux Doillon
- Department of Radiology, American Memorial Hospital, 47 rue Cognacq-jay, FR
| | - Carole Durot
- Department of Radiology, American Memorial Hospital, 47 rue Cognacq-jay, FR
| | - Claire Pluchart
- Department of Oncopediatrics, American Memorial Hospital, 47 rue Cognacq-jay, FR
| | - Claude Marcus
- Department of Radiology, American Memorial Hospital, 47 rue Cognac q-jay, FR
| | - Manel Djelouah
- Department of Radiology, American Memorial Hospital, 47 rue Cognacq-jay, FR
| | - Aline Carsin-Vu
- Department of Radiology, American Memorial Hospital, 47 rue Cognacq-jay, FR
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Sollini M, Kirienko M, Cavinato L, Ricci F, Biroli M, Ieva F, Calderoni L, Tabacchi E, Nanni C, Zinzani PL, Fanti S, Guidetti A, Alessi A, Corradini P, Seregni E, Carlo-Stella C, Chiti A. Methodological framework for radiomics applications in Hodgkin's lymphoma. Eur J Hybrid Imaging 2020; 4:9. [PMID: 34191173 PMCID: PMC8218114 DOI: 10.1186/s41824-020-00078-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 05/06/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND According to published data, radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet. PURPOSE The study aimed at setting up a methodological framework in radiomics applications in Hodgkin's lymphoma (HL), especially at (a) developing a novel feature selection approach, (b) evaluating radiomic intra-patient lesions' similarity, and (c) classifying relapsing refractory (R/R) vs non-(R/R) patients. METHODS We retrospectively included 85 patients (male:female = 52:33; median age 35 years, range 19-74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis-transformed features were used to build the fingerprints that were tested to assess lesions' similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersampling Boosting of Tree Ensemble (RUBTE). RESULTS HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity). CONCLUSIONS Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R performed the best when all the lesions were used.
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Affiliation(s)
- Martina Sollini
- Humanitas University, Via Rita Levi Montalcini 4, MI 20090 Pieve Emanuele, Italy
- Humanitas Clinical and Research Center – IRCCS -, via Manzoni 56, 20089 Rozzano, MI Italy
| | - Margarita Kirienko
- Humanitas University, Via Rita Levi Montalcini 4, MI 20090 Pieve Emanuele, Italy
| | - Lara Cavinato
- Humanitas Clinical and Research Center – IRCCS -, via Manzoni 56, 20089 Rozzano, MI Italy
- MOX–Modelling and Scientific Computing lab., Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Francesca Ricci
- Humanitas Clinical and Research Center – IRCCS -, via Manzoni 56, 20089 Rozzano, MI Italy
| | - Matteo Biroli
- Humanitas University, Via Rita Levi Montalcini 4, MI 20090 Pieve Emanuele, Italy
| | - Francesca Ieva
- MOX–Modelling and Scientific Computing lab., Department of Mathematics, Politecnico di Milano, Milano, Italy
- CADS–Center for Analysis, Decision, and Society, Human Technopole, Milano, Italy
| | | | | | | | - Pier Luigi Zinzani
- Institute of Hematology “Seràgnoli”, University of Bologna, Bologna, Italy
| | - Stefano Fanti
- Nuclear Medicine, AOU S.Orsola-Malpighi, Bologna, Italy
| | - Anna Guidetti
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
- University of Milan, Milan, Italy
| | | | - Paolo Corradini
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
- University of Milan, Milan, Italy
| | - Ettore Seregni
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Carmelo Carlo-Stella
- Humanitas University, Via Rita Levi Montalcini 4, MI 20090 Pieve Emanuele, Italy
- Humanitas Clinical and Research Center – IRCCS -, via Manzoni 56, 20089 Rozzano, MI Italy
| | - Arturo Chiti
- Humanitas University, Via Rita Levi Montalcini 4, MI 20090 Pieve Emanuele, Italy
- Humanitas Clinical and Research Center – IRCCS -, via Manzoni 56, 20089 Rozzano, MI Italy
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Current status and quality of radiomics studies in lymphoma: a systematic review. Eur Radiol 2020; 30:6228-6240. [PMID: 32472274 DOI: 10.1007/s00330-020-06927-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/25/2020] [Accepted: 04/28/2020] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To perform a systematic review regarding the developments in the field of radiomics in lymphoma. To evaluate the quality of included articles by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), the phases classification criteria for image mining studies, and the radiomics quality scoring (RQS) tool. METHODS We searched for eligible articles in the MEDLINE/PubMed and EMBASE databases using the terms "radiomics", "texture" and "lymphoma". The included studies were divided into two categories: diagnosis-, therapy response- and outcome-related studies. The diagnosis-related studies were evaluated using the QUADAS-2; all studies were evaluated using the phases classification criteria for image mining studies and the RQS tool by two reviewers. RESULTS Forty-five studies were included; thirteen papers (28.9%) focused on the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). Thirty-two (71.1%) studies were classified as discovery science according to the phase classification criteria for image mining studies. The mean RQS score of all studies was 14.2% (ranging from 0.0 to 40.3%), and 23 studies (51.1%) were given a score of < 10%. CONCLUSION The radiomics features could serve as diagnostic and prognostic indicators in lymphoma. However, the current conclusions should be interpreted with caution due to the suboptimal quality of the studies. In order to introduce radiomics into lymphoma clinical settings, the lesion segmentation and selection, the influence of the pathological pattern and the extraction of multiple modalities and multiple time points features need to be further studied. KEY POINTS • The radiomics approach may provide useful information for diagnosis, prediction of the therapy response, and outcome of lymphoma. • The quality of published radiomics studies in lymphoma has been suboptimal to date. • More studies are needed to examine lesion selection and segmentation, the influence of pathological patterns, and the extraction of multiple modalities and multiple time point features.
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Kuno H, Garg N, Qureshi MM, Chapman MN, Li B, Meibom SK, Truong MT, Takumi K, Sakai O. CT Texture Analysis of Cervical Lymph Nodes on Contrast-Enhanced [ 18F] FDG-PET/CT Images to Differentiate Nodal Metastases from Reactive Lymphadenopathy in HIV-Positive Patients with Head and Neck Squamous Cell Carcinoma. AJNR Am J Neuroradiol 2019; 40:543-550. [PMID: 30792253 DOI: 10.3174/ajnr.a5974] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 01/05/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND AND PURPOSE Differentiating nodal metastases from reactive adenopathy in HIV-infected patients with [18F] FDG-PET/CT can be challenging because lymph nodes in HIV-positive patients often show increased [18F] FDG uptake. The purpose of this study was to assess CT textural analysis characteristics of HIV-positive and HIV-negative lymph nodes on [18F] FDG-PET/CT to differentiate nodal metastases from disease-specific nodal reactivity. MATERIALS AND METHODS Nine HIV-positive patients with head and neck squamous cell carcinoma (7 men, 2 women; 29-62 years of age; median age, 48 years) with 22 lymph nodes (≥1 cm) who underwent contrast-enhanced CT with [18F] FDG-PET followed by pathologic evaluation of cervical lymph nodes were retrospectively reviewed. Twenty-six HIV-negative patients with head and neck squamous cell carcinoma with 61 lymph nodes were evaluated as a control group. Each lymph node was manually segmented, and an in-house-developed Matlab-based texture analysis program extracted 41 texture features from each segmented volume. A mixed linear regression model was used to compare the pathologically proved malignant lymph nodes with benign nodes in the 2 enrolled groups. RESULTS Thirteen (59%) lymph nodes in the HIV-positive group and 22 (36%) lymph nodes in the HIV-negative control group were confirmed as positive for metastases. There were 7 histogram features (P = .017-0.032), 3 gray-level co-occurrence features (P = .009-.025), and 9 gray-level run-length features (P < .001-.033) that demonstrated a significant difference in HIV-positive patients with either benign or malignant lymph nodes. CONCLUSIONS CT texture analysis may be useful as a noninvasive method of obtaining additional quantitative information to differentiate nodal metastases from disease-specific nodal reactivity in HIV-positive patients with head and neck squamous cell carcinoma.
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Affiliation(s)
- H Kuno
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Department of Diagnostic Radiology (H.K.), National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - N Garg
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - M M Qureshi
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Radiation Oncology (M.M.Q., M.T.T., O.S.)
| | - M N Chapman
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - B Li
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - S K Meibom
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.)
| | - M T Truong
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Radiation Oncology (M.M.Q., M.T.T., O.S.)
| | - K Takumi
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.).,Department of Radiology (K.T.), Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - O Sakai
- From the Departments of Radiology (H.K., N.G., M.M.Q., M.N.C., B.L., S.K.M., M.T.T., K.T., O.S.) .,Radiation Oncology (M.M.Q., M.T.T., O.S.).,Otolaryngology-Head and Neck Surgery (O.S.), Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
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Hou Z, Li S, Ren W, Liu J, Yan J, Wan S. Radiomic analysis in T2W and SPAIR T2W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma. J Thorac Dis 2018; 10:2256-2267. [PMID: 29850130 DOI: 10.21037/jtd.2018.03.123] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background To investigate the capability of radiomic analysis using T2-weighted (T2W) and spectral attenuated inversion-recovery T2-weighted (SPAIR T2W) magnetic resonance imaging (MRI) for predicting the therapeutic response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Methods Pretreatment T2W- and SPAIR T2W-MRI of 68 ESCC patients (37 responders, 31 nonresponders) were analyzed. A number of 138 radiomic features were extracted from each image sequence respectively. Kruskal-Wallis test were performed to evaluate the capability of each feature on treatment response classification. Sensitivity and specificity for each of the studied features were derived using receiver operating characteristic (ROC) analysis. Support vector machine (SVM) and artificial neural network (ANN) models were constructed based on the training set (23 responders, 20 nonresponders) for the prediction of treatment response, and then the testing set (14 responders, 11 nonresponders) validated the reliability of the models. Comparison between the performances of the models was performed by using McNemar's test. Results Radiomic analysis showed significance in the prediction of treatment response. The analyses showed that complete responses (CRs) versus stable diseases (SDs), partial responses (PRs) versus SDs, and responders (CRs and PRs) versus nonresponders (SDs) could be differentiated by 26, 17, and 33 features (T2W: 11/11/15, SPAIR T2W: 15/6/18), respectively. The prediction models (ANN and SVM) based on features extracted from SPAIR T2W sequence (SVM: 0.929, ANN: 0.883) showed higher accuracy than those derived from T2W (SVM: 0.893, ANN: 0.861). No statistical difference was observed in the performance of the two classifiers (P=0.999). Conclusions Radiomic analysis based on pretreatment T2W- and SPAIR T2W-MRI can be served as imaging biomarkers to predict treatment response to CRT in ESCC patients.
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Affiliation(s)
- Zhen Hou
- State Key Laboratory of Bioelectronics, Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Shuangshuang Li
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Wei Ren
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Juan Liu
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Jing Yan
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Suiren Wan
- State Key Laboratory of Bioelectronics, Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
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11
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Shaikh F, Franc B, Allen E, Sala E, Awan O, Hendrata K, Halabi S, Mohiuddin S, Malik S, Hadley D, Shrestha R. Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise. J Am Coll Radiol 2018; 15:543-549. [PMID: 29366598 DOI: 10.1016/j.jacr.2017.12.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 12/07/2017] [Indexed: 12/18/2022]
Abstract
Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.
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Affiliation(s)
- Faiq Shaikh
- Institute of Computational Health Sciences, UCSF, San Francisco, California.
| | - Benjamin Franc
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California
| | | | - Evis Sala
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Omer Awan
- Department of Radiology, Temple University, Philadelphia, Pennsylvania
| | | | - Safwan Halabi
- Department of Radiology, Stanford University, Palo Alto, California
| | - Sohaib Mohiuddin
- Department of Radiology, Division of Nuclear Medicine, University of Miami, Miami, Florida
| | - Sana Malik
- School of Social Welfare, Stony Brook University, New York, New York
| | - Dexter Hadley
- Institute of Computational Health Sciences, UCSF, San Francisco, California
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12
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Dextraze K, Saha A, Kim D, Narang S, Lehrer M, Rao A, Narang S, Rao D, Ahmed S, Madhugiri V, Fuller CD, Kim MM, Krishnan S, Rao G, Rao A. Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma. Oncotarget 2017; 8:112992-113001. [PMID: 29348883 PMCID: PMC5762568 DOI: 10.18632/oncotarget.22947] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 11/20/2017] [Indexed: 12/28/2022] Open
Abstract
Glioblastoma (GBM) show significant inter- and intra-tumoral heterogeneity, impacting response to treatment and overall survival time of 12-15 months. To study glioblastoma phenotypic heterogeneity, multi-parametric magnetic resonance images (MRI) of 85 glioblastoma patients from The Cancer Genome Atlas were analyzed to characterize tumor-derived spatial habitats for their relationship with outcome (overall survival) and to identify their molecular correlates (i.e., determine associated tumor signaling pathways correlated with imaging-derived habitat measurements). Tumor sub-regions based on four sequences (fluid attenuated inversion recovery, T1-weighted, post-contrast T1-weighted, and T2-weighted) were defined by automated segmentation. From relative intensity of pixels in the 3-dimensional tumor region, "imaging habitats" were identified and analyzed for their association to clinical and genetic data using survival modeling and Dirichlet regression, respectively. Sixteen distinct tumor sub-regions ("spatial imaging habitats") were derived, and those associated with overall survival (denoted "relevant" habitats) in glioblastoma patients were identified. Dirichlet regression implicated each relevant habitat with unique pathway alterations. Relevant habitats also had some pathways and cellular processes in common, including phosphorylation of STAT-1 and natural killer cell activity, consistent with cancer hallmarks. This work revealed clinical relevance of MRI-derived spatial habitats and their relationship with oncogenic molecular mechanisms in patients with GBM. Characterizing the associations between imaging-derived phenotypic measurements with the genomic and molecular characteristics of tumors can enable insights into tumor biology, further enabling the practice of personalized cancer treatment. The analytical framework and workflow demonstrated in this study are inherently scalable to multiple MR sequences.
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Affiliation(s)
- Katherine Dextraze
- Department of Medical Physics, The University of Texas Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Abhijoy Saha
- Department of Statistics, The Ohio State University, Columbus, OH, USA
| | - Donnie Kim
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shivali Narang
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael Lehrer
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anita Rao
- Texas Academy of Math and Science, Denton, TX, USA.,School of Engineering and Applied Sciences, Columbia University, New York City, NY, USA
| | - Saphal Narang
- Debakey High School for Health Professions, Houston, TX, USA
| | - Dinesh Rao
- Radiology, University of Florida, College of Medicine, Jacksonville, FL, USA
| | - Salmaan Ahmed
- Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Clifton David Fuller
- Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michelle M Kim
- Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA
| | - Sunil Krishnan
- Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ganesh Rao
- Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Arvind Rao
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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13
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Ben Bouallègue F, Tabaa YA, Kafrouni M, Cartron G, Vauchot F, Mariano-Goulart D. Association between textural and morphological tumor indices on baseline PET-CT and early metabolic response on interim PET-CT in bulky malignant lymphomas. Med Phys 2017; 44:4608-4619. [DOI: 10.1002/mp.12349] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 05/11/2017] [Accepted: 05/11/2017] [Indexed: 12/22/2022] Open
Affiliation(s)
- Fayçal Ben Bouallègue
- Nuclear Medicine Department; Gui de Chauliac University Hospital; 80, Avenue Augustin Fliche 34295 Montpellier Cedex 5 France
| | - Yassine Al Tabaa
- Nuclear Medicine Department; Gui de Chauliac University Hospital; 80, Avenue Augustin Fliche 34295 Montpellier Cedex 5 France
| | - Marilyne Kafrouni
- Nuclear Medicine Department; Gui de Chauliac University Hospital; 80, Avenue Augustin Fliche 34295 Montpellier Cedex 5 France
| | - Guillaume Cartron
- Haematology Department; Saint Eloi University Hospital; 80, Avenue Augustin Fliche 34295 Montpellier Cedex 5 France
| | - Fabien Vauchot
- Nuclear Medicine Department; Gui de Chauliac University Hospital; 80, Avenue Augustin Fliche 34295 Montpellier Cedex 5 France
| | - Denis Mariano-Goulart
- Nuclear Medicine Department; Gui de Chauliac University Hospital; 80, Avenue Augustin Fliche 34295 Montpellier Cedex 5 France
- U1046 INSERM - UMR9214 CNRS; CHU Arnaud de Villeneuve; 371 Avenue du Doyen Giraud 34295 Montpellier Cedex 5 France
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14
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Ganeshan B, Miles KA, Babikir S, Shortman R, Afaq A, Ardeshna KM, Groves AM, Kayani I. CT-based texture analysis potentially provides prognostic information complementary to interim fdg-pet for patients with hodgkin's and aggressive non-hodgkin's lymphomas. Eur Radiol 2017; 27:1012-1020. [PMID: 27380902 PMCID: PMC5306313 DOI: 10.1007/s00330-016-4470-8] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Revised: 06/03/2016] [Accepted: 06/07/2016] [Indexed: 12/21/2022]
Abstract
OBJECTIVES The purpose of this study was to investigate the ability of computed tomography texture analysis (CTTA) to provide additional prognostic information in patients with Hodgkin's lymphoma (HL) and high-grade non-Hodgkin's lymphoma (NHL). METHODS This retrospective, pilot-study approved by the IRB comprised 45 lymphoma patients undergoing routine 18F-FDG-PET-CT. Progression-free survival (PFS) was determined from clinical follow-up (mean-duration: 40 months; range: 10-62 months). Non-contrast-enhanced low-dose CT images were submitted to CTTA comprising image filtration to highlight features of different sizes followed by histogram-analysis using kurtosis. Prognostic value of CTTA was compared to PET FDG-uptake value, tumour-stage, tumour-bulk, lymphoma-type, treatment-regime, and interim FDG-PET (iPET) status using Kaplan-Meier analysis. Cox regression analysis determined the independence of significantly prognostic imaging and clinical features. RESULTS A total of 27 patients had aggressive NHL and 18 had HL. Mean PFS was 48.5 months. There was no significant difference in pre-treatment CTTA between the lymphoma sub-types. Kaplan-Meier analysis found pre-treatment CTTA (medium feature scale, p=0.010) and iPET status (p<0.001) to be significant predictors of PFS. Cox analysis revealed that an interaction between pre-treatment CTTA and iPET status was the only independent predictor of PFS (HR: 25.5, 95% CI: 5.4-120, p<0.001). Specifically, pre-treatment CTTA risk stratified patients with negative iPET. CONCLUSION CTTA can potentially provide prognostic information complementary to iPET for patients with HL and aggressive NHL. KEY POINTS • CT texture-analysis (CTTA) provides prognostic information complementary to interim FDG-PET in Lymphoma. • Pre-treatment CTTA and interim PET status were significant predictors of progression-free survival. • Patients with negative interim PET could be further stratified by pre-treatment CTTA. • Provide precision surveillance where additional imaging reserved for patients at greatest recurrence-risk. • Assists in risk-adapted treatment strategy based on interim PET and CTTA.
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Affiliation(s)
- B Ganeshan
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK.
| | - K A Miles
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
| | - S Babikir
- Human Health Division, Nuclear Medicine and Diagnostic Imaging Section, International Atomic Energy Agency (IAEA), Vienna, Austria
| | - R Shortman
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
| | - A Afaq
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
| | - K M Ardeshna
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
| | - A M Groves
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
| | - I Kayani
- Institute of Nuclear Medicine, University College London, Euston Rd, London, UK
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15
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Liu Y, Liu S, Qu F, Li Q, Cheng R, Ye Z. Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: association with pathologic grade. Oncotarget 2017; 8:53664-53674. [PMID: 28881840 PMCID: PMC5581139 DOI: 10.18632/oncotarget.15399] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Accepted: 01/31/2017] [Indexed: 12/21/2022] Open
Abstract
Objectives To investigate whether texture features on contrast-enhanced computed tomography (CECT) images of lung adenocarcinoma have association with pathologic grade. Methods A cohort of 148 patients with surgically operated adenocarcinoma was retrospectively reviewed. Fifty-four CT features of the primary lung tumor were extracted from CECT images using open-source 3D Slicer software; meanwhile, enhancement homogeneity was evaluated by two radiologists using visual assessment. Multivariate logistic regression analysis was performed to determine significant image indicator of pathologic grade. Results Tumors of intermediate grade were more likely to be never smokers (P=0.020). Enhancement heterogeneity by visual assessment showed no statistical difference between intermediate grade and high grade (P=0.671). Among those 54 features, 29 of them were significantly associated with pathologic grade. Multivariate logistic regression analyses identified F33 (Homogeneity 1) (P=0.005) and F38 (Inverse Variance) (P=0.032) as unique independent image indicators of pathologic grade, and the AUC calculated from this model (AUC=0.834) was higher than clinical model (AUC=0.615) (P=0.0001). Conclusions Our study revealed that texture analysis on CECT images could be helpful in predicting pathologic grade of lung adenocarcinoma.
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Affiliation(s)
- Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Shichang Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Fangyuan Qu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Qian Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Runfen Cheng
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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16
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van Rossum PSN, Xu C, Fried DV, Goense L, Court LE, Lin SH. The emerging field of radiomics in esophageal cancer: current evidence and future potential. Transl Cancer Res 2016; 5:410-423. [PMID: 30687593 DOI: 10.21037/tcr.2016.06.19] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
'Radiomics' is the name given to the emerging field of extracting additional information from standard medical images using advanced feature analysis. This innovative form of quantitative image analysis appears to have future potential for clinical practice in patients with esophageal cancer by providing an additional layer of information to the standard imaging assessment. There is a growing body of evidence suggesting that radiomics may provide incremental value for staging, predicting treatment response, and predicting survival in esophageal cancer, for which the current work-up has substantial limitations. This review outlines the available evidence and future potential for the application of radiomics in the management of patients with esophageal cancer. In addition, an overview of the current evidence on the importance of reproducibility of image features and the substantial influence of varying smoothing scales, quantization levels, and segmentation methods is provided.
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Affiliation(s)
- Peter S N van Rossum
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston (Texas), USA.,Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cai Xu
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston (Texas), USA.,Department of Radiation Oncology, Cancer Hospital & Institute, Chinese Academy of Medical Science, Beijing 100021, China
| | - David V Fried
- Department of Radiation Oncology, University of North Carolina, Chapel Hill (North Carolina), USA
| | - Lucas Goense
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston (Texas), USA
| | - Steven H Lin
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston (Texas), USA
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17
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Knogler T, El-Rabadi K, Weber M, Karanikas G, Mayerhoefer ME. Erratum: “Three-dimensional texture analysis of contrast enhanced CT images for treatment response assessment in Hodgkin lymphoma: Comparison with F-18-FDG PET” [Med. Phys. 41(12), 121904 (2014)]. Med Phys 2015; 42:4998. [DOI: 10.1118/1.4923801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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