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Heo J, Sim Y, Kim BM, Kim DJ, Kim YD, Nam HS, Choi YS, Lee SK, Kim EY, Sohn B. Radiomics using non-contrast CT to predict hemorrhagic transformation risk in stroke patients undergoing revascularization. Eur Radiol 2024; 34:6005-6015. [PMID: 38308679 DOI: 10.1007/s00330-024-10618-6] [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: 08/28/2023] [Revised: 01/09/2024] [Accepted: 01/12/2024] [Indexed: 02/05/2024]
Abstract
OBJECTIVES This study explores whether textural features from initial non-contrast CT scans of infarcted brain tissue are linked to hemorrhagic transformation susceptibility. MATERIALS AND METHODS Stroke patients undergoing thrombolysis or thrombectomy from Jan 2012 to Jan 2022 were analyzed retrospectively. Hemorrhagic transformation was defined using follow-up magnetic resonance imaging. A total of 94 radiomic features were extracted from the infarcted tissue on initial NCCT scans. Patients were divided into training and test sets (7:3 ratio). Two models were developed with fivefold cross-validation: one incorporating first-order and textural radiomic features, and another using only textural radiomic features. A clinical model was also constructed using logistic regression with clinical variables, and test set validation was performed. RESULTS Among 362 patients, 218 had hemorrhagic transformations. The LightGBM model with all radiomics features had the best performance, with an area under the receiver operating characteristic curve (AUROC) of 0.986 (95% confidence interval [CI], 0.971-1.000) on the test dataset. The ExtraTrees model performed best when textural features were employed, with an AUROC of 0.845 (95% CI, 0.774-0.916). Minimum, maximum, and ten percentile values were significant predictors of hemorrhagic transformation. The clinical model showed an AUROC of 0.544 (95% CI, 0.431-0.658). The performance of the radiomics models was significantly better than that of the clinical model on the test dataset (p < 0.001). CONCLUSIONS The radiomics model can predict hemorrhagic transformation using NCCT in stroke patients. Low Hounsfield unit was a strong predictor of hemorrhagic transformation, while textural features alone can predict hemorrhagic transformation. CLINICAL RELEVANCE STATEMENT Using radiomic features extracted from initial non-contrast computed tomography, early prediction of hemorrhagic transformation has the potential to improve patient care and outcomes by aiding in personalized treatment decision-making and early identification of at-risk patients. KEY POINTS • Predicting hemorrhagic transformation following thrombolysis in stroke is challenging since multiple factors are associated. • Radiomics features of infarcted tissue on initial non-contrast CT are associated with hemorrhagic transformation. • Textural features on non-contrast CT are associated with the frailty of the infarcted tissue.
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Affiliation(s)
- JoonNyung Heo
- Department of Neurology, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong, South Korea
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Yongsik Sim
- Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea
| | - Byung Moon Kim
- Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea
| | - Dong Joon Kim
- Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea
| | - Young Dae Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyo Suk Nam
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Yoon Seong Choi
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Seung-Koo Lee
- Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea
| | - Eung Yeop Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, South Korea
| | - Beomseok Sohn
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, South Korea.
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Della Corte A, Mori M, Calabrese F, Palumbo D, Ratti F, Palazzo G, Pellegrini A, Santangelo D, Ronzoni M, Spezi E, Del Vecchio A, Fiorino C, Aldrighetti L, De Cobelli F. Preoperative MRI radiomic analysis for predicting local tumor progression in colorectal liver metastases before microwave ablation. Int J Hyperthermia 2024; 41:2349059. [PMID: 38754994 DOI: 10.1080/02656736.2024.2349059] [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: 12/19/2023] [Accepted: 04/25/2024] [Indexed: 05/18/2024] Open
Abstract
PURPOSE Radiomics may aid in predicting prognosis in patients with colorectal liver metastases (CLM). Consistent data is available on CT, yet limited data is available on MRI. This study assesses the capability of MRI-derived radiomic features (RFs) to predict local tumor progression-free survival (LTPFS) in patients with CLMs treated with microwave ablation (MWA). METHODS All CLM patients with pre-operative Gadoxetic acid-MRI treated with MWA in a single institution between September 2015 and February 2022 were evaluated. Pre-procedural information was retrieved retrospectively. Two observers manually segmented CLMs on T2 and T1-Hepatobiliary phase (T1-HBP) scans. After inter-observer variability testing, 148/182 RFs showed robustness on T1-HBP, and 141/182 on T2 (ICC > 0.7).Cox multivariate analysis was run to establish clinical (CLIN-mod), radiomic (RAD-T1, RAD-T2), and combined (COMB-T1, COMB-T2) models for LTPFS prediction. RESULTS Seventy-six CLMs (43 patients) were assessed. Median follow-up was 14 months. LTP occurred in 19 lesions (25%).CLIN-mod was composed of minimal ablation margins (MAMs), intra-segment progression and primary tumor grade and exhibited moderately high discriminatory power in predicting LTPFS (AUC = 0.89, p = 0.0001). Both RAD-T1 and RAD-T2 were able to predict LTPFS: (RAD-T1: AUC = 0.83, p = 0.0003; RAD-T2: AUC = 0.79, p = 0.001). Combined models yielded the strongest performance (COMB-T1: AUC = 0.98, p = 0.0001; COMB-T2: AUC = 0.95, p = 0.0003). Both combined models included MAMs and tumor regression grade; COMB-T1 also featured 10th percentile of signal intensity, while tumor flatness was present in COMB-T2. CONCLUSION MRI-based radiomic evaluation of CLMs is feasible and potentially useful for LTP prediction. Combined models outperformed clinical or radiomic models alone for LTPFS prediction.
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Affiliation(s)
- Angelo Della Corte
- Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy
- University Vita-Salute San Raffaele, Milan, Italy
| | - Martina Mori
- Department of Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | | | - Diego Palumbo
- Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy
- University Vita-Salute San Raffaele, Milan, Italy
| | - Francesca Ratti
- Hepatobiliary Surgery Division, IRCCS San Raffaele Hospital, Milan, Italy
| | - Gabriele Palazzo
- Department of Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | | | | | - Monica Ronzoni
- Unit of Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, UK
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, UK
| | | | - Claudio Fiorino
- Department of Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | - Luca Aldrighetti
- University Vita-Salute San Raffaele, Milan, Italy
- Hepatobiliary Surgery Division, IRCCS San Raffaele Hospital, Milan, Italy
| | - Francesco De Cobelli
- Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy
- University Vita-Salute San Raffaele, Milan, Italy
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O'Shea R, Withey SJ, Owczarczyk K, Rookyard C, Gossage J, Godfrey E, Jobling C, Parsons SL, Skipworth RJE, Goh V. Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma. Eur Radiol 2024:10.1007/s00330-024-10666-y. [PMID: 38526750 DOI: 10.1007/s00330-024-10666-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Personalising management of primary oesophageal adenocarcinoma requires better risk stratification. Lack of independent validation of proposed imaging biomarkers has hampered clinical translation. We aimed to prospectively validate previously identified prognostic grey-level co-occurrence matrix (GLCM) CT features for 3-year overall survival. METHODS Following ethical approval, clinical and contrast-enhanced CT data were acquired from participants from five institutions. Data from three institutions were used for training and two for testing. Survival classifiers were modelled on prespecified variables ('Clinical' model: age, clinical T-stage, clinical N-stage; 'ClinVol' model: clinical features + CT tumour volume; 'ClinRad' model: ClinVol features + GLCM_Correlation and GLCM_Contrast). To reflect current clinical practice, baseline stage was also modelled as a univariate predictor ('Stage'). Discrimination was assessed by area under the receiver operating curve (AUC) analysis; calibration by Brier scores; and clinical relevance by thresholding risk scores to achieve 90% sensitivity for 3-year mortality. RESULTS A total of 162 participants were included (144 male; median 67 years [IQR 59, 72]; training, 95 participants; testing, 67 participants). Median survival was 998 days [IQR 486, 1594]. The ClinRad model yielded the greatest test discrimination (AUC, 0.68 [95% CI 0.54, 0.81]) that outperformed Stage (ΔAUC, 0.12 [95% CI 0.01, 0.23]; p = .04). The Clinical and ClinVol models yielded comparable test discrimination (AUC, 0.66 [95% CI 0.51, 0.80] vs. 0.65 [95% CI 0.50, 0.79]; p > .05). Test sensitivity of 90% was achieved by ClinRad and Stage models only. CONCLUSIONS Compared to Stage, multivariable models of prespecified clinical and radiomic variables yielded improved prediction of 3-year overall survival. CLINICAL RELEVANCE STATEMENT Previously identified radiomic features are prognostic but may not substantially improve risk stratification on their own. KEY POINTS • Better risk stratification is needed in primary oesophageal cancer to personalise management. • Previously identified CT features-GLCM_Correlation and GLCM_Contrast-contain incremental prognostic information to age and clinical stage. • Compared to staging, multivariable clinicoradiomic models improve discrimination of 3-year overall survival.
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Affiliation(s)
- Robert O'Shea
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Samuel J Withey
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Radiology, Royal Marsden Hospital NHS Trust, Sutton, Surrey, UK
| | - Kasia Owczarczyk
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Clinical Oncology, Guy's & St Thomas' Hospitals NHS Foundation Trust, London, UK
| | - Christopher Rookyard
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - James Gossage
- Department of Surgery, Guy's & St Thomas' Hospitals NHS Foundation Trust, London, UK
| | - Edmund Godfrey
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Craig Jobling
- Department of Radiology, Nottingham University Hospitals NHS Foundation Trust, Nottingham, UK
| | - Simon L Parsons
- Department of Surgery, Nottingham University Hospitals NHS Foundation Trust, Nottingham, UK
| | | | - Vicky Goh
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Radiology, Guy's & St Thomas' Hospitals NHS Foundation Trust, Westminster Bridge Road, London, SE1 7EG, UK.
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Mori M, Deantoni C, Olivieri M, Spezi E, Chiara A, Baroni S, Picchio M, Del Vecchio A, Di Muzio NG, Fiorino C, Dell'Oca I. External validation of an 18F-FDG-PET radiomic model predicting survival after radiotherapy for oropharyngeal cancer. Eur J Nucl Med Mol Imaging 2023; 50:1329-1336. [PMID: 36604325 DOI: 10.1007/s00259-022-06098-9] [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: 08/12/2022] [Accepted: 12/24/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE/OBJECTIVE The purpose of the study is to externally validate published 18F-FDG-PET radiomic models for outcome prediction in patients with oropharyngeal cancer treated with chemoradiotherapy. MATERIAL/METHODS Outcome data and pre-radiotherapy PET images of 100 oropharyngeal cancer patients (stage IV:78) treated with concomitant chemotherapy to 66-69 Gy/30 fr were available. Tumors were segmented using a previously validated semi-automatic method; 450 radiomic features (RF) were extracted according to IBSI (Image Biomarker Standardization Initiative) guidelines. Only one model for cancer-specific survival (CSS) prediction was suitable to be independently tested, according to our criteria. This model, in addition to HPV status, SUVmean and SUVmax, included two independent meta-factors (Fi), resulting from combining selected RF clusters. In a subgroup of 66 patients with complete HPV information, the global risk score R was computed considering the original coefficients and was tested by Cox regression as predictive of CSS. Independently, only the radiomic risk score RF derived from Fi was tested on the same subgroup to learn about the radiomics contribution to the model. The metabolic tumor volume (MTV) was also tested as a single predictor and its prediction performances were compared to the global and radiomic models. Finally, the validation of MTV and the radiomic score RF were also tested on the entire dataset. RESULTS Regarding the analysis of the subgroup with HPV information, with a median follow-up of 41.6 months, seven patients died due to cancer. R was confirmed to be associated to CSS (p value = 0.05) with a C-index equal 0.75 (95% CI=0.62-0.85). The best cut-off value (equal to 0.15) showed high ability in patient stratification (p=0.01, HR=7.4, 95% CI=1.6-11.4). The 5-year CSS for R were 97% (95% CI: 93-100%) vs 74% (56-92%) for low- and high-risk groups, respectively. RF and MTV alone were also significantly associated to CSS for the subgroup with an almost identical C-index. According to best cut-off value (RF>0.12 and MTV>15.5cc), the 5-year CSS were 96% (95% CI: 89-100%) vs 65% (36-94%) and 97% (95% CI: 88-100%) vs 77% (58-93%) for RF and MTV, respectively. Results regarding RF and MTV were confirmed in the overall group. CONCLUSION A previously published PET radiomic model for CSS prediction was independently validated. Performances of the model were similar to the ones of using only the MTV, without improvement of prediction accuracy.
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Affiliation(s)
- Martina Mori
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Chiara Deantoni
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Michela Olivieri
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, UK
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, UK
| | - Anna Chiara
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Simone Baroni
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Maria Picchio
- Department of Nuclear Medicine, San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | | | - Nadia Gisella Di Muzio
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Claudio Fiorino
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
| | - Italo Dell'Oca
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
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Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning. JOURNAL OF ONCOLOGY 2022; 2022:8534262. [PMID: 36147442 PMCID: PMC9489385 DOI: 10.1155/2022/8534262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/26/2022] [Accepted: 08/13/2022] [Indexed: 11/18/2022]
Abstract
Purpose To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC). Methods Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort, n = 216; test cohort, n = 92). We extracted 207 handcrafted radiomic features and 1000 deep radiomic features of lymph nodes from their computed tomography (CT) images. The t-test and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimensions and select key features. Handcrafted radiomics, deep radiomics, and clinical features were combined to construct models. Models I (handcrafted radiomic features), II (Model I plus deep radiomic features), and III (Model II plus clinical features) were built using three machine learning methods: support vector machine (SVM), adaptive boosting (AdaBoost), and random forest (RF). The best model was compared with the results of two radiologists, and its performance was evaluated in terms of sensitivity, specificity, accuracy, area under the curve (AUC), and receiver operating characteristic (ROC) curve analysis. Results No significant differences were observed between cohorts. Ten handcrafted and 12 deep radiomic features were selected from the extracted features (p < 0.05). Model III could discriminate between patients with and without LNM better than the diagnostic results of the two radiologists. Conclusion The combination of handcrafted radiomic features, deep radiomic features, and clinical features could be used clinically to assess lymph node status in patients with ESCC.
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O'Shea RJ, Rookyard C, Withey S, Cook GJR, Tsoka S, Goh V. Radiomic assessment of oesophageal adenocarcinoma: a critical review of 18F-FDG PET/CT, PET/MRI and CT. Insights Imaging 2022; 13:104. [PMID: 35715706 PMCID: PMC9206060 DOI: 10.1186/s13244-022-01245-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/28/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVES Radiomic models present an avenue to improve oesophageal adenocarcinoma assessment through quantitative medical image analysis. However, model selection is complicated by the abundance of available predictors and the uncertainty of their relevance and reproducibility. This analysis reviews recent research to facilitate precedent-based model selection for prospective validation studies. METHODS This analysis reviews research on 18F-FDG PET/CT, PET/MRI and CT radiomics in oesophageal adenocarcinoma between 2016 and 2021. Model design, testing and reporting are evaluated according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score and Radiomics Quality Score (RQS). Key results and limitations are analysed to identify opportunities for future research in the area. RESULTS Radiomic models of stage and therapeutic response demonstrated discriminative capacity, though clinical applications require greater sensitivity. Although radiomic models predict survival within institutions, generalisability is limited. Few radiomic features have been recommended independently by multiple studies. CONCLUSIONS Future research must prioritise prospective validation of previously proposed models to further clinical translation.
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Affiliation(s)
- Robert J O'Shea
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK.
| | - Chris Rookyard
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK
| | - Sam Withey
- Department of Radiology, The Royal Marsden NHS Foundation Trust, London, UK
| | - Gary J R Cook
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK
- King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, UK
| | - Sophia Tsoka
- Department of Informatics, School of Natural and Mathematical Sciences, King's College London, London, UK
| | - Vicky Goh
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
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Radiomics utilization to differentiate nonfunctional adenoma in essential hypertension and functional adenoma in primary aldosteronism. Sci Rep 2022; 12:8892. [PMID: 35614110 PMCID: PMC9132956 DOI: 10.1038/s41598-022-12835-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 05/12/2022] [Indexed: 11/08/2022] Open
Abstract
We performed the present study to investigate the role of computed tomography (CT) radiomics in differentiating nonfunctional adenoma and aldosterone-producing adenoma (APA) and outcome prediction in patients with clinically suspected primary aldosteronism (PA). This study included 60 patients diagnosed with essential hypertension (EH) with nonfunctional adenoma on CT and 91 patients with unilateral surgically proven APA. Each whole nodule on unenhanced and venous phase CT images was segmented manually and randomly split into training and test sets at a ratio of 8:2. Radiomic models for nodule discrimination and outcome prediction of APA after adrenalectomy were established separately using the training set by least absolute shrinkage and selection operator (LASSO) logistic regression, and the performance was evaluated on test sets. The model can differentiate adrenal nodules in EH and PA with a sensitivity, specificity, and accuracy of 83.3%, 78.9% and 80.6% (AUC = 0.91 [0.72, 0.97]) in unenhanced CT and 81.2%, 100% and 87.5% (AUC = 0.98 [0.77, 1.00]) in venous phase CT, respectively. In the outcome after adrenalectomy, the models showed a favorable ability to predict biochemical success (Unenhanced/venous CT: AUC = 0.67 [0.52, 0.79]/0.62 [0.46, 0.76]) and clinical success (Unenhanced/venous CT: AUC = 0.59 [0.47, 0.70]/0.64 [0.51, 0.74]). The results showed that CT-based radiomic models hold promise to discriminate APA and nonfunctional adenoma when an adrenal incidentaloma was detected on CT images of hypertensive patients in clinical practice, while the role of radiomic analysis in outcome prediction after adrenalectomy needs further investigation.
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Brancato V, Garbino N, Mannelli L, Aiello M, Salvatore M, Franzese M, Cavaliere C. Impact of radiogenomics in esophageal cancer on clinical outcomes: A pilot study. World J Gastroenterol 2021; 27:6110-6127. [PMID: 34629823 PMCID: PMC8476334 DOI: 10.3748/wjg.v27.i36.6110] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/16/2021] [Accepted: 07/30/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Esophageal cancer (ESCA) is the sixth most common malignancy in the world, and its incidence is rapidly increasing. Recently, several microRNAs (miRNAs) and messenger RNA (mRNA) targets were evaluated as potential biomarkers and regulators of epigenetic mechanisms involved in early diagnosis. In addition, computed tomography (CT) radiomic studies on ESCA improved the early stage identification and the prediction of response to treatment. Radiogenomics provides clinically useful prognostic predictions by linking molecular characteristics such as gene mutations and gene expression patterns of malignant tumors with medical images and could provide more opportunities in the management of patients with ESCA.
AIM To explore the combination of CT radiomic features and molecular targets associated with clinical outcomes for characterization of ESCA patients.
METHODS Of 15 patients with diagnosed ESCA were included in this study and their CT imaging and transcriptomic data were extracted from The Cancer Imaging Archive and gene expression data from The Cancer Genome Atlas, respectively. Cancer stage, history of significant alcohol consumption and body mass index (BMI) were considered as clinical outcomes. Radiomic analysis was performed on CT images acquired after injection of contrast medium. In total, 1302 radiomics features were extracted from three-dimensional regions of interest by using PyRadiomics. Feature selection was performed using a correlation filter based on Spearman’s correlation (ρ) and Wilcoxon-rank sum test respect to clinical outcomes. Radiogenomic analysis involved ρ analysis between radiomic features associated with clinical outcomes and transcriptomic signatures consisting of eight N6-methyladenosine RNA methylation regulators and five up-regulated miRNA. The significance level was set at P < 0.05.
RESULTS Of 25, five and 29 radiomic features survived after feature selection, considering stage, alcohol history and BMI as clinical outcomes, respectively. Radiogenomic analysis with stage as clinical outcome revealed that six of the eight mRNA regulators and two of the five up-regulated miRNA were significantly correlated with ten and three of the 25 selected radiomic features, respectively (-0.61 < ρ < -0.60 and 0.53 < ρ < 0.69, P < 0.05). Assuming alcohol history as clinical outcome, no correlation was found between the five selected radiomic features and mRNA regulators, while a significant correlation was found between one radiomic feature and three up-regulated miRNAs (ρ = -0.56, ρ = -0.64 and ρ = 0.61, P < 0.05). Radiogenomic analysis with BMI as clinical outcome revealed that four mRNA regulators and one up-regulated miRNA were significantly correlated with 10 and two radiomic features, respectively (-0.67 < ρ < -0.54 and 0.53 < ρ < 0.71, P < 0.05).
CONCLUSION Our study revealed interesting relationships between the expression of eight N6-methyladenosine RNA regulators, as well as five up-regulated miRNAs, and CT radiomic features associated with clinical outcomes of ESCA patients.
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Su CW, Lee JC, Chang YF, Su NW, Lee PH, Dai KY, Tai HC, Leu YS, Chen YJ. Delta-volume radiomics of induction chemotherapy to predict outcome of subsequent chemoradiotherapy for locally advanced hypopharyngeal cancer. TUMORI JOURNAL 2021; 108:450-460. [PMID: 34423708 DOI: 10.1177/03008916211039018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Induction chemotherapy (IC) followed by concurrent chemoradiotherapy (CCRT) is recommended for larynx-preserving treatment of locally advanced hypopharyngeal cancer (LAHC). However, the conventional evaluation of response is not robust enough to predict the outcome of subsequent treatments. This study aimed to develop an imaging biomarker using changes in radiomic features in invasive tumor front (ITF) by IC to predict treatment outcome of subsequent CCRT in LAHC. METHODS From 2006 to 2018, 59 computed tomography (CT) scan images before and after IC in patients with LAHC were used to contour the gross tumor volumes (GTVs). A total of 48 delta-volume radiomics features were acquired from the absolute spatial difference of GTVs (delta-GTV) before and after IC, conceptually representing a consistent portion of ITF. Least absolute shrinkage and selection operator regression (LASSO) was used to select features for establishing the model generating radiomic score (R score). RESULTS A model including 5 radiomic features from delta-GTV to predict better progression-free survival (PFS) of patients receiving subsequent CCRT was established. The R score was validated with all datasets (area under the curve 0.77). Low R score (<-0.16) was associated with improved PFS (p < 0.05). CONCLUSIONS The established radiomic model for ITF from radiomic features of delta-GTV after IC might be a potential imaging biomarker for predicting clinical outcome of subsequent CCRT in LAHC.
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Affiliation(s)
- Che-Wei Su
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei
| | - Jehn-Chuan Lee
- Department of Otorhinolaryngology, MacKay Memorial Hospital, Taipei
| | - Yi-Fang Chang
- Department of Hematology and Oncology, MacKay Memorial Hospital, Taipei
| | - Nai-Wen Su
- Department of Hematology and Oncology, MacKay Memorial Hospital, Taipei
| | - Pei-Hsuan Lee
- Department of International Business, National Chengchi University, Taipei
| | - Kun-Yao Dai
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei
| | - Hung-Chi Tai
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei
| | - Yi-Shing Leu
- Department of Otorhinolaryngology, MacKay Memorial Hospital, Taipei
| | - Yu-Jen Chen
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei.,Department of Nursing, MacKay Junior College of Medicine, Nursing, and Management, Taipei.,Department of Medical Research, China Medical University Hospital, Taichung
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Fiz F, Costa G, Gennaro N, la Bella L, Boichuk A, Sollini M, Politi LS, Balzarini L, Torzilli G, Chiti A, Viganò L. Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment. Diagnostics (Basel) 2021; 11:diagnostics11071162. [PMID: 34202253 PMCID: PMC8305553 DOI: 10.3390/diagnostics11071162] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/11/2021] [Accepted: 06/22/2021] [Indexed: 12/29/2022] Open
Abstract
The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017–2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.
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Affiliation(s)
- Francesco Fiz
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Correspondence: (F.F.); (L.V.); Tel.: +39-02-8224-7361 (L.V.)
| | - Guido Costa
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
| | - Nicolò Gennaro
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
| | - Ludovico la Bella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Alexandra Boichuk
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Martina Sollini
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Letterio S. Politi
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Luca Balzarini
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Arturo Chiti
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Luca Viganò
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
- Correspondence: (F.F.); (L.V.); Tel.: +39-02-8224-7361 (L.V.)
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van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging 2020; 11:91. [PMID: 32785796 PMCID: PMC7423816 DOI: 10.1186/s13244-020-00887-2] [Citation(s) in RCA: 561] [Impact Index Per Article: 140.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 06/22/2020] [Indexed: 02/06/2023] Open
Abstract
Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features.The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical "how-to" guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.
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Affiliation(s)
- Janita E van Timmeren
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Davide Cester
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
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