ArticleRadiology. Artificial intelligence2019
Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma.
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Article in Radiology. Artificial intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 2 of them syntheses that pooled it.
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73citing papers in PubMed, 2 pooled it
11.3field-weighted citation impact, top 1% of its field
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Who cites it
73 citing papers in PubMed, 2 syntheses or guidelines pooled it, 116 citations in OpenAlex.
- Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.BMC pulmonary medicine · 2024Pooled it
- Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review.European radiology · 2023Pooled it
- Trial
- A radio-genomics biomarker for precision epidermal growth factor receptor mutation targeting therapy in non-small cell lung cancer.Scientific reports · 2026Article
- Regionally modulated radiomics analysis in PET/CT imaging: application to prognosis prediction of head and neck cancer.Physical and engineering sciences in medicine · 2026Article
- Quantitative peri-lesional densitometry mapping via thin-slice volume rendering enhances differentiation of pneumonic-type lung cancer and inflammatory pneumonia.Frontiers in oncology · 2026Article
- Imaging biomarkers related to tumor-associated macrophage in immunotherapy treatment planning for non-small cell lung cancer.Translational lung cancer research · 2025Article
- Utilizing intratumoral and peritumoral ultrasound radiomics for predicting KRAS gene mutation status in rectal cancer patients.Journal of applied clinical medical physics · 2025Article
- Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer.NPJ precision oncology · 2025Article
- Integrative radiomics analysis of peri-tumoral and habitat zones for predicting major pathological response to neoadjuvant immunotherapy and chemotherapy in non-small cell lung cancer.Translational lung cancer research · 2025Article
- The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software.BMC cancer · 2025Article
- Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity.Journal of imaging informatics in medicine · 2025Article
- CT-based radiomics for prediction of response to neoadjuvant immunochemotherapy in patients with esophageal carcinoma.Frontiers in oncology · 2025Article
- Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.La Radiologia medica · 2025Article
- Application of machine learning based on habitat imaging and vision transformer to predict treatment response of locally advanced esophageal squamous cell carcinoma following neoadjuvant chemoimmunotherapy: a multi-center study.Frontiers in immunology · 2025Article
- Integration of intratumoral/peritumoral radiomics and deep learning for predicting overall survival in non-small cell lung cancer patients: a multicenter study.Frontiers in oncology · 2025Article
- Radiomics-based decision support tool with ground-glass opacity status of 5-year survival prediction for early-stage non-small cell lung cancer.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Article
- Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC.NPJ precision oncology · 2024Article
- Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy.Insights into imaging · 2024Article
- Prediction of early clinical response to neoadjuvant chemotherapy in Triple-negative breast cancer: Incorporating Radiomics through breast MRI.Scientific reports · 2024Article
13 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors at 1 institution in 1 country.
Mohammadhadi KhorramiDepartment of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).ORCID 0000-0002-4141-8548
Monica KhungerDepartment of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).
Alexia ZagourasDepartment of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).
Pradnya PatilDepartment of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).
Rajat ThawaniDepartment of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).ORCID 0000-0002-5378-9434
Kaustav BeraDepartment of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).
Prabhakar RajiahDepartment of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).ORCID 0000-0001-7538-385X
Pingfu FuDepartment of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).ORCID 0000-0002-2334-5218
Vamsidhar VelchetiDepartment of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).
Anant MadabhushiDepartment of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).
Cleveland Clinic · US
Funding
Comprehensive quantitative ultrafast 3D liver MRIR01DK098503 · NIDDK · CASE WESTERN RESERVE UNIVERSITY · PI GULANI, VIKAS, SEIBERLICH, NICOLE · 2013 to 2017
$2.6MComputerized morphologic-molecular predictor of progression in DCISR21CA195152 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI BADVE, SUNIL S., MADABHUSHI, ANANT · 2015 to 2016
$388kBIOMEDICAL ENGINEERING RESEARCH FACILITIESC06RR012463 · NCRR · CASE WESTERN RESERVE UNIVERSITY · PI KUTINA, KENNETH L · 1997 to 1997
–NCI NIH HHS R21 CA195152NCRR NIH HHS C06 RR012463NIDDK NIH HHS R01 DK098503
Abstract
purposeTo identify the role of radiomics texture features both within and outside the nodule in predicting MATERIALS AND
methodsData in a total of 125 patients who had been treated with pemetrexed-based platinum doublet chemotherapy at Cleveland Clinic were retrospectively analyzed. The patients were divided randomly into two sets with the constraint that there were an equal number of responders and nonresponders in the training set. The training set comprised 53 patients with NSCLC, and the validation set comprised 72 patients. A machine learning classifier trained with radiomic texture features extracted from intra- and peritumoral regions of non-contrast-enhanced CT images was used to predict response to chemotherapy. The radiomic risk-score signature was generated by using least absolute shrinkage and selection operator with the Cox regression model; association of the radiomic signature with TTP and OS was also evaluated.
resultsA combination of radiomic features in conjunction with a quadratic discriminant analysis classifier yielded a mean maximum area under the receiver operating characteristic curve (AUC) of 0.82 ± 0.09 (standard deviation) in the training set and a corresponding AUC of 0.77 in the independent testing set. The radiomics signature was also significantly associated with TTP (hazard ratio [HR], 2.8; 95% confidence interval [CI]: 1.95, 4.00;
conclusionThis study suggests that radiomic texture features extracted from within and around the nodule on baseline CT scans are
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