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Bobholz SA, Lowman AK, Connelly JM, Duenweg SR, Winiarz A, Nath B, Kyereme F, Brehler M, Bukowy J, Coss D, Lupo JM, Phillips JJ, Ellingson BM, Krucoff MO, Mueller WM, Banerjee A, LaViolette PS. Noninvasive Autopsy-Validated Tumor Probability Maps Identify Glioma Invasion Beyond Contrast Enhancement. Neurosurgery 2024; 95:537-547. [PMID: 38501824 PMCID: PMC11302944 DOI: 10.1227/neu.0000000000002898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/09/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND AND OBJECTIVES This study identified a clinically significant subset of patients with glioma with tumor outside of contrast enhancement present at autopsy and subsequently developed a method for detecting nonenhancing tumor using radio-pathomic mapping. We tested the hypothesis that autopsy-based radio-pathomic tumor probability maps would be able to noninvasively identify areas of infiltrative tumor beyond traditional imaging signatures. METHODS A total of 159 tissue samples from 65 subjects were aligned to MRI acquired nearest to death for this retrospective study. Demographic and survival characteristics for patients with and without tumor beyond the contrast-enhancing margin were computed. An ensemble algorithm was used to predict pixelwise tumor presence from pathological annotations using segmented cellularity (Cell), extracellular fluid, and cytoplasm density as input (6 train/3 test subjects). A second level of ensemble algorithms was used to predict voxelwise Cell, extracellular fluid, and cytoplasm on the full data set (43 train/22 test subjects) using 5-by-5 voxel tiles from T1, T1 + C, fluid-attenuated inversion recovery, and apparent diffusion coefficient as input. The models were then combined to generate noninvasive whole brain maps of tumor probability. RESULTS Tumor outside of contrast was identified in 41.5% of patients, who showed worse survival outcomes (hazard ratio = 3.90, P < .001). Tumor probability maps reliably tracked nonenhancing tumor on a range of local and external unseen data, identifying tumor outside of contrast in 69% of presurgical cases that also showed reduced survival outcomes (hazard ratio = 1.67, P = .027). CONCLUSION This study developed a multistage model for mapping gliomas using autopsy tissue samples as ground truth, which was able to identify regions of tumor beyond traditional imaging signatures.
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Affiliation(s)
- Samuel A. Bobholz
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Allison K. Lowman
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jennifer M. Connelly
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Savannah R. Duenweg
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Biprojit Nath
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Fitzgerald Kyereme
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Michael Brehler
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - John Bukowy
- Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee, Wisconsin, USA
| | - Dylan Coss
- Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Janine M. Lupo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco and Berkeley, California, USA
| | - Joanna J. Phillips
- Department of Neurological Surgery, University of California, San Francisco, California, USA
- Department of Pathology, University of California, San Francisco, California, USA
| | - Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Max O. Krucoff
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Wade M. Mueller
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anjishnu Banerjee
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Peter S. LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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2
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Pecchi A, Bozzola C, Beretta C, Besutti G, Toss A, Cortesi L, Balboni E, Nocetti L, Ligabue G, Torricelli P. DCE-MRI Radiomic analysis in triple negative ductal invasive breast cancer. Comparison between BRCA and not BRCA mutated patients: Preliminary results. Magn Reson Imaging 2024:110214. [PMID: 39047852 DOI: 10.1016/j.mri.2024.110214] [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: 05/13/2024] [Revised: 07/17/2024] [Accepted: 07/20/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVE The research aimed to determine whether and which radiomic features from breast dynamic contrast enhanced (DCE) MRI could predict the presence of BRCA1 mutation in patients with triple-negative breast cancer (TNBC). MATERIAL AND METHODS This retrospective study included consecutive patients histologically diagnosed with TNBC who underwent breast DCE-MRI in 2010-2021. Baseline DCE-MRIs were retrospectively reviewed; percentage maps of wash-in and wash-out were computed and breast lesions were manually segmented, drawing a 5 mm-Region of Interest (ROI) inside the tumor and another 5 mm-ROI inside the contralateral healthy gland. Features for each map and each ROI were extracted with Pyradiomics-3D Slicer and considered first separately (tumor and contralateral gland) and then together. In each analysis the more important features for BRCA1 status classification were selected with Maximum Relevance Minimum Redundancy algorithm and used to fit four classifiers. RESULTS The population included 67 patients and 86 lesions (21 in BRCA1-mutated, 65 in non BRCA-carriers). The best classifiers for BRCA mutation were Support Vector Classifier and Logistic Regression in models fitted with both gland and tumor features, reaching an Area Under ROC Curve (AUC) of 0.80 (SD 0.21) and of 0.79 (SD 0.20), respectively. Three features were higher in BRCA1-mutated compared to non BRCA-mutated: Total Energy and Correlation from gray level cooccurrence matrix, both measured in contralateral gland in wash-out maps, and Root Mean Squared, selected from the wash-out map of the tumor. CONCLUSIONS This study showed the feasibility of a radiomic study with breast DCE-MRI and the potential of radiomics in predicting BRCA1 mutational status.
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Affiliation(s)
- Annarita Pecchi
- Department of Radiology, University Hospital of Modena, 41224 Modena, Italy; Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy
| | - Chiara Bozzola
- Department of Radiology, University Hospital of Modena, 41224 Modena, Italy; Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy
| | - Cecilia Beretta
- Department of Radiology, University Hospital of Modena, 41224 Modena, Italy; Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy
| | - Giulia Besutti
- Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy; Radiology Unit, Department of Diagnostic Imaging and Laboratory Medicine, AUSL-IRCCS di Reggio Emilia, Via Risorgimento 80, 42123 Reggio Emilia, Italy.
| | - Angela Toss
- Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy; Department of Oncology and Haematology, Azienda Ospedaliero-Universitaria di Modena, Modena, Italy
| | - Laura Cortesi
- Department of Oncology and Haematology, Azienda Ospedaliero-Universitaria di Modena, Modena, Italy
| | - Erica Balboni
- Medical Physics Unit, University Hospital of Modena, 41125 Modena, Italy
| | - Luca Nocetti
- Medical Physics Unit, University Hospital of Modena, 41125 Modena, Italy
| | - Guido Ligabue
- Department of Radiology, University Hospital of Modena, 41224 Modena, Italy; Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy
| | - Pietro Torricelli
- Department of Radiology, University Hospital of Modena, 41224 Modena, Italy; Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy
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Huynh LM, Swanson S, Cima S, Haddadin E, Baine M. Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication. Cancers (Basel) 2024; 16:1897. [PMID: 38791977 PMCID: PMC11120365 DOI: 10.3390/cancers16101897] [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: 03/17/2024] [Revised: 04/24/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and quantitatively analyzing subvisual features in medical imaging. Within this context, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in PC risk stratification. A stepwise literature search of publications from 2017 to 2023 was performed. Of 23 articles on PSMA PET/CT-derived prostate radiomics, PC diagnosis, prediction of biopsy Gleason score (GS), prediction of adverse pathology, and treatment outcomes were the primary endpoints of 4 (17.4%), 5 (21.7%), 7 (30.4%), and 7 (30.4%) studies, respectively. In predicting PC diagnosis, PSMA PET/CT-derived models performed well, with receiver operator characteristic curve area under the curve (ROC-AUC) values of 0.85-0.925. Similarly, in the prediction of biopsy and surgical pathology results, ROC-AUC values had ranges of 0.719-0.84 and 0.84-0.95, respectively. Finally, prediction of recurrence, progression, or survival following treatment was explored in nine studies, with ROC-AUC ranging 0.698-0.90. Of the 23 studies included in this review, 2 (8.7%) included external validation. While explorations of PSMA PET/CT-derived radiomic models are immature in follow-up and experience, these results represent great potential for future investigation and exploration. Prior to consideration for clinical use, however, rigorous validation in feature reproducibility and biologic validation of radiomic signatures must be prioritized.
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Affiliation(s)
- Linda My Huynh
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (L.M.H.); (S.C.)
- Department of Urology, University of California, Irvine, CA 92868, USA;
| | - Shea Swanson
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (L.M.H.); (S.C.)
| | - Sophia Cima
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (L.M.H.); (S.C.)
| | - Eliana Haddadin
- Department of Urology, University of California, Irvine, CA 92868, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (L.M.H.); (S.C.)
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Bobholz SA, Hoefs A, Hamburger J, Lowman AK, Winiarz A, Duenweg SR, Kyereme F, Connelly J, Coss D, Krucoff M, Banerjee A, LaViolette PS. Radio-pathomic maps of glioblastoma identify phenotypes of non-enhancing tumor infiltration associated with bevacizumab treatment response. J Neurooncol 2024; 167:233-241. [PMID: 38372901 PMCID: PMC11024025 DOI: 10.1007/s11060-024-04593-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 01/30/2024] [Indexed: 02/20/2024]
Abstract
BACKGROUND Autopsy-based radio-pathomic maps of glioma pathology have shown substantial promise inidentifying areas of non-enhancing tumor presence, which may be able to differentiate subsets of patients that respond favorably to treatments such as bevacizumab that have shown mixed efficacy evidence. We tested the hypthesis that phenotypes of non-enhancing tumor fronts can distinguish between glioblastoma patients that will respond favorably to bevacizumab and will visually capture treatment response. METHODS T1, T1C, FLAIR, and ADC images were used to generate radio-pathomic maps of tumor characteristics for 79 pre-treatment patients with a primary GBM or high-grade IDH1-mutant astrocytoma for this study. Novel phenotyping (hypercellular, hypocellular, hybrid, or well-circumscribed front) of the non-enhancing tumor front was performed on each case. Kaplan Meier analyses were then used to assess differences in survival and bevacizumab efficacy between phenotypes. Phenotype compartment segmentations generated longitudinally for a subset of 26 patients over the course of bevacizumab treatment, where a mixed effect model was used to detect longitudinal changes. RESULTS Well-Circumscribed patients showed significant/trending increases in survival compared to Hypercellular Front (HR = 2.0, p = 0.05), Hypocellular Front (HR = 2.02, p = 0.03), and Hybrid Front tumors (HR = 1.75, p = 0.09). Only patients with hypocellular or hybrid fronts showed significant survival benefits from bevacizumab treatment (HR = 2.35, p = 0.02; and HR = 2.45, p = 0.03, respectively). Hypocellular volumes decreased by an average 50.52 mm3 per day of bevacizumab treatment (p = 0.002). CONCLUSION Patients with a hypocellular tumor front identified by radio-pathomic maps showed improved treatment efficacy when treated with bevacizumab, and reducing hypocellular volumes over the course of treatment may indicate treatment response.
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Affiliation(s)
- Samuel A Bobholz
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd, 53226, Milwaukee, WI, USA
| | - Alisha Hoefs
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd, 53226, Milwaukee, WI, USA
| | - Jordyn Hamburger
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd, 53226, Milwaukee, WI, USA
| | - Allison K Lowman
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd, 53226, Milwaukee, WI, USA
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Savannah R Duenweg
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Fitzgerald Kyereme
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd, 53226, Milwaukee, WI, USA
| | - Jennifer Connelly
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dylan Coss
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Max Krucoff
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Anjishnu Banerjee
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Peter S LaViolette
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd, 53226, Milwaukee, WI, USA.
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA.
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, USA.
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Li X, Cheng Y, Han X, Cui B, Li J, Yang H, Xu G, Lin Q, Xiao X, Tang J, Lu J. Exploring the association of glioma tumor residuals from incongruent [ 18F]FET PET/MR imaging with tumor proliferation using a multiparametric MRI radiomics nomogram. Eur J Nucl Med Mol Imaging 2024; 51:779-796. [PMID: 37864593 DOI: 10.1007/s00259-023-06468-x] [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: 06/05/2023] [Accepted: 09/28/2023] [Indexed: 10/23/2023]
Abstract
PURPOSE The study aimed to using multiparametric MRI radiomics to predict glioma tumor residuals (TRFET over MR) derived from incongruent [18F]fluoroethyl-L-tyrosine ([18F]FET) PET/MR imaging. METHODS One hundred ten patients with gliomas who underwent [18F]FET PET/MR scanning were retrospectively analyzed. The TRFET over MR was identified by the discrepancy-PET that the extent of resection (EOR) based on MRI subtracted the biological tumor volume on PET images. The MRI parameters and radiomics features were extracted based on EOR and selected by the least absolute shrinkage and selection operator to construct radiomics score (Rad-score). The correlation network analysis of all features was analyzed by Spearman's correlation tests. The methods for evaluating the clinical usefulness consisted of the receiver operating characteristic curve, the calibration curve, and decision curve analysis. RESULTS The Rad-score of the patients with the TRFET over MR was significantly higher than those with the non TRFET over MR (p < 0.001). The Rad-score was significantly correlated with the discrepancy-PET (r = 0.72, p < 0.001), Ki-67 level (r = 0.76, p < 0.001), and epidermal growth factor receptor (EGFR) of gliomas (r = 0.75, p < 0.001), respectively. Moreover, there was a difference of the correlation network analysis between the TRPET over MR group and non TRFET over MR group. The nomogram combing Rad-score and clinical features had the greatest performance in predicting TRFET over MR (AUC = 0.90/0.87, training/testing). There was a significant difference in prognosis (median OS, 17 m vs. 43 m) between patients with TRFET over MR and non TRFET over MR based on nomogram prediction (p < 0.001). CONCLUSION The nomogram based on MRI radiomics would predict gliomas tumor residuals caused by the absence of 18F-PET PET examination and adjust EOR to improve prognosis.
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Affiliation(s)
- Xiaoran Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Ye Cheng
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xin Han
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Bixiao Cui
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Jing Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Hongwei Yang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Geng Xu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Qingtang Lin
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xinru Xiao
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Tang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China.
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Bobholz SA, Hoefs A, Hamburger J, Lowman AK, Winiarz A, Duenweg SR, Kyereme F, Connelly J, Coss D, Krucoff M, Banerjee A, LaViolette PS. Radio-pathomic maps of glioblastoma identify phenotypes of non-enhancing tumor infiltration associated with bevacizumab treatment response. RESEARCH SQUARE 2024:rs.3.rs-3832221. [PMID: 38260400 PMCID: PMC10802733 DOI: 10.21203/rs.3.rs-3832221/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background Autopsy-based radio-pathomic maps of glioma pathology have shown substantial promise inidentifying areas of non-enhancing tumor presence, which may be able to differentiate subsets of patients that respond favorably to treatments such as bevacizumab that have shown mixed efficacy evidence. We tested the hypthesis that phenotypes of non-enhancing tumor fronts can distinguish between glioblastoma patients that will respond favorably to bevacizumab and will visually capture treatment response. Methods T1, T1C, FLAIR, and ADC images were used to generate radio-pathomic maps of tumor characteristics for 79 pre-treatment patients with a primary GBM or high-grade IDH1-mutant astrocytoma for this study. Novel phenotyping (hypercellular, hypocellular, hybrid, or well-circumscribed front) of the non-enhancing tumor front was performed on each case. Kaplan Meier analyses were then used to assess differences in survival and bevacizumab efficacy between phenotypes. Phenotype compartment segmentations generated longitudinally for a subset of 26 patients over the course of bevacizumab treatment, where a mixed effect model was used to detect longitudinal changes. Results Well-Circumscribed patients showed significant/trending increases in survival compared to Hypercellular Front (HR = 2.0, p = 0.05), Hypocellular Front (HR = 2.02, p = 0.03), and Hybrid Front tumors (HR = 1.75, p = 0.09). Only patients with hypocellular or hybrid fronts showed significant survival benefits from bevacizumab treatment (HR = 2.35, p = 0.02; and HR = 2.45, p = 0.03, respectively). Hypocellular volumes decreased by an average 50.52 mm3 per day of bevacizumab treatment (p = 0.002). Conclusion Patients with a hypocellular tumor front identified by radio-pathomic maps showed improved treatment efficacy when treated with bevacizumab, and reducing hypocellular volumes over the course of treatment may indicate treatment response.
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7
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Zhao Z, Song Z, Wang Z, Zhang F, Ding Z, Fan T. Advances in Molecular Pathology, Diagnosis and Treatment of Spinal Cord Astrocytomas. Technol Cancer Res Treat 2024; 23:15330338241262483. [PMID: 39043042 PMCID: PMC11271101 DOI: 10.1177/15330338241262483] [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: 02/17/2024] [Revised: 05/23/2024] [Accepted: 05/28/2024] [Indexed: 07/25/2024] Open
Abstract
Spinal cord astrocytoma (SCA) is a rare subtype of astrocytoma, posing challenges in diagnosis and treatment. Low-grade SCA can achieve long-term survival solely through surgery, while high-grade has a disappointing prognosis even with comprehensive treatment. Diagnostic criteria and standard treatment of intracranial astrocytoma have shown obvious limitations in SCA. Research on the molecular mechanism in SCA is lagging far behind that on intracranial astrocytoma. In recent years, huge breakthroughs have been made in molecular pathology of astrocytoma, and novel techniques have emerged, including DNA methylation analysis and radiomics. These advances are now making it possible to provide a precise diagnosis and develop corresponding treatment strategies in SCA. Our aim is to review the current status of diagnosis and treatment of SCA, and summarize the latest research advancement, including tumor subtype, molecular characteristics, diagnostic technology, and potential therapy strategies, thus deepening our understanding of this uncommon tumor type and providing guidance for accurate diagnosis and treatment.
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Affiliation(s)
- Zijun Zhao
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Zihan Song
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zairan Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Fan Zhang
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Ze Ding
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Tao Fan
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
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Wang P, Xie S, Wu Q, Weng L, Hao Z, Yuan P, Zhang C, Gao W, Wang S, Zhang H, Song Y, He J, Gao Y. Model incorporating multiple diffusion MRI features: development and validation of a radiomics-based model to predict adult-type diffuse gliomas grade. Eur Radiol 2023; 33:8809-8820. [PMID: 37439936 PMCID: PMC10667393 DOI: 10.1007/s00330-023-09861-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/06/2023] [Accepted: 05/14/2023] [Indexed: 07/14/2023]
Abstract
OBJECTIVES To develop and validate a radiomics-based model (ADGGIP) for predicting adult-type diffuse gliomas (ADG) grade by combining multiple diffusion modalities and clinical and imaging morphologic features. METHODS In this prospective study, we recruited 103 participants diagnosed with ADG and collected their preoperative conventional MRI and multiple diffusion imaging (diffusion tensor imaging, diffusion kurtosis imaging, neurite orientation dispersion and density imaging, and mean apparent propagator diffusion-MRI) data in our hospital, as well as clinical information. Radiomic features of the diffusion images and clinical information and morphological data from the radiological reports were extracted, and multiple pipelines were used to construct the optimal model. Model validation was performed through a time-independent validation cohort. ROC curves were used to evaluate model performance. The clinical benefit was determined by decision curve analysis. RESULTS From June 2018 to May 2021, 72 participants were recruited for the training cohort. Between June 2021 and February 2022, 31 participants were enrolled in the prospective validation cohort. In the training cohort (AUC 0.958), internal validation cohort (0.942), and prospective validation cohort (0.880), ADGGIP had good accuracy in predicting ADG grade. ADGGIP was also significantly better than the single-modality prediction model (AUC 0.860) and clinical imaging morphology model (0.841) (all p < .01) in the prospective validation cohort. When the threshold probability was greater than 5%, ADGGIP provided the greatest net benefit. CONCLUSION ADGGIP, which is based on advanced diffusion modalities, can predict the grade of ADG with high accuracy and robustness and can help improve clinical decision-making. CLINICAL RELEVANCE STATEMENT Integrated multi-modal predictive modeling is beneficial for early detection and treatment planning of adult-type diffuse gliomas, as well as for investigating the genuine clinical significance of biomarkers. KEY POINTS • Integrated model exhibits the highest performance and stability. • When the threshold is greater than 5%, the integrated model has the greatest net benefit. • The advanced diffusion models do not demonstrate better performance than the simple technology.
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Affiliation(s)
- Peng Wang
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
- Inner Mongolia Medical University, Hohhot, 010110, China
| | - Shenghui Xie
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Qiong Wu
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Lixin Weng
- Inner Mongolia Medical University, Hohhot, 010110, China
- Department of Pathology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Zhiyue Hao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Pengxuan Yuan
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Chi Zhang
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Weilin Gao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Shaoyu Wang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Huapeng Zhang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Jinlong He
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China.
| | - Yang Gao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China.
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Duenweg SR, Bobholz SA, Barrett MJ, Lowman AK, Winiarz A, Nath B, Stebbins M, Bukowy J, Iczkowski KA, Jacobsohn KM, Vincent-Sheldon S, LaViolette PS. T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence. Cancers (Basel) 2023; 15:4437. [PMID: 37760407 PMCID: PMC10526331 DOI: 10.3390/cancers15184437] [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: 06/30/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.
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Affiliation(s)
- Savannah R. Duenweg
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Samuel A. Bobholz
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Michael J. Barrett
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Allison K. Lowman
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Biprojit Nath
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Margaret Stebbins
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - John Bukowy
- Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, 1025 N Broadway, Milwaukee, WI 53202, USA
| | - Kenneth A. Iczkowski
- Department of Pathology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA;
| | - Kenneth M. Jacobsohn
- Department of Urology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Stephanie Vincent-Sheldon
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Peter S. LaViolette
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
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10
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Schregel K, Heinz L, Hunger J, Pan C, Bode J, Fischer M, Sturm V, Venkataramani V, Karimian-Jazi K, Agardy DA, Streibel Y, Zerelles R, Wick W, Heiland S, Bunse T, Tews B, Platten M, Winkler F, Bendszus M, Breckwoldt MO. A Cellular Ground Truth to Develop MRI Signatures in Glioma Models by Correlative Light Sheet Microscopy and Atlas-Based Coregistration. J Neurosci 2023; 43:5574-5587. [PMID: 37429718 PMCID: PMC10376935 DOI: 10.1523/jneurosci.1470-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 05/21/2023] [Accepted: 06/25/2023] [Indexed: 07/12/2023] Open
Abstract
Glioblastoma is the most common malignant primary brain tumor with poor overall survival. Magnetic resonance imaging (MRI) is the main imaging modality for glioblastoma but has inherent shortcomings. The molecular and cellular basis of MR signals is incompletely understood. We established a ground truth-based image analysis platform to coregister MRI and light sheet microscopy (LSM) data to each other and to an anatomic reference atlas for quantification of 20 predefined anatomic subregions. Our pipeline also includes a segmentation and quantification approach for single myeloid cells in entire LSM datasets. This method was applied to three preclinical glioma models in male and female mice (GL261, U87MG, and S24), which exhibit different key features of the human glioma. Multiparametric MR data including T2-weighted sequences, diffusion tensor imaging, T2 and T2* relaxometry were acquired. Following tissue clearing, LSM focused on the analysis of tumor cell density, microvasculature, and innate immune cell infiltration. Correlated analysis revealed differences in quantitative MRI metrics between the tumor-bearing and the contralateral hemisphere. LSM identified tumor subregions that differed in their MRI characteristics, indicating tumor heterogeneity. Interestingly, MRI signatures, defined as unique combinations of different MRI parameters, differed greatly between the models. The direct correlation of MRI and LSM allows an in-depth characterization of preclinical glioma and can be used to decipher the structural, cellular, and, likely, molecular basis of tumoral MRI biomarkers. Our approach may be applied in other preclinical brain tumor or neurologic disease models, and the derived MRI signatures could ultimately inform image interpretation in a clinical setting.SIGNIFICANCE STATEMENT We established a histologic ground truth-based approach for MR image analyses and tested this method in three preclinical glioma models exhibiting different features of glioblastoma. Coregistration of light sheet microscopy to MRI allowed for an evaluation of quantitative MRI data in histologically distinct tumor subregions. Coregistration to a mouse brain atlas enabled a regional comparison of MRI parameters with a histologically informed interpretation of the results. Our approach is transferable to other preclinical models of brain tumors and further neurologic disorders. The method can be used to decipher the structural, cellular, and molecular basis of MRI signal characteristics. Ultimately, information derived from such analyses could strengthen the neuroradiological evaluation of glioblastoma as they enhance the interpretation of MRI data.
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Affiliation(s)
- Katharina Schregel
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Lennart Heinz
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Jessica Hunger
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Chenchen Pan
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Department of Neurology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Julia Bode
- Molecular Mechanisms of Tumor Invasion, Schaller Research Group at the University of Heidelberg and the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Manuel Fischer
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Volker Sturm
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Varun Venkataramani
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Department of Neurology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, 69120 Heidelberg, Germany
| | - Kianush Karimian-Jazi
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Dennis A Agardy
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational Neurosciences, Heidelberg University, 68167 Mannheim, Germany
| | - Yannik Streibel
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Roland Zerelles
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Department of Neurology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Theresa Bunse
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational Neurosciences, Heidelberg University, 68167 Mannheim, Germany
| | - Björn Tews
- Molecular Mechanisms of Tumor Invasion, Schaller Research Group at the University of Heidelberg and the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Michael Platten
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational Neurosciences, Heidelberg University, 68167 Mannheim, Germany
| | - Frank Winkler
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Department of Neurology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Michael O Breckwoldt
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
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11
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Faraz K, Dauce G, Bouhamama A, Leporq B, Sasaki H, Bito Y, Beuf O, Pilleul F. Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches. J Pers Med 2023; 13:1062. [PMID: 37511674 PMCID: PMC10382057 DOI: 10.3390/jpm13071062] [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: 05/31/2023] [Revised: 06/24/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, and IDC vs. ILC classification tasks. The best results were obtained for the ER+ vs. ER- and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers.
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Affiliation(s)
- Khuram Faraz
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Grégoire Dauce
- FUJIFILM Healthcare France S.A.S., 69800 Saint-Priest, France
| | - Amine Bouhamama
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
- Department of Radiology, Centre Léon Bérard, 69008 Lyon, France
| | - Benjamin Leporq
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Hajime Sasaki
- FUJIFILM Healthcare France S.A.S., 69800 Saint-Priest, France
- FUJIFILM Healthcare Corporation, Tokyo 107-0052, Japan
| | | | - Olivier Beuf
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Frank Pilleul
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
- Department of Radiology, Centre Léon Bérard, 69008 Lyon, France
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12
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Cui J, Miao X, Yanghao X, Qin X. Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases. Front Neurol 2023; 14:1171167. [PMID: 37360350 PMCID: PMC10288367 DOI: 10.3389/fneur.2023.1171167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/16/2023] [Indexed: 06/28/2023] Open
Abstract
Background The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies. Methods Publications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection. Results On October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. "Glioma" is a current attractive research hotspot. Keywords such as "machine learning," "brain metastasis," and "gene mutations" have recently appeared at the research frontier. Conclusion Most of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro-environment of tumors.
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13
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Brancato V, Cavaliere C, Garbino N, Isgrò F, Salvatore M, Aiello M. The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study. Front Oncol 2022; 12:1005805. [PMID: 36276163 PMCID: PMC9582951 DOI: 10.3389/fonc.2022.1005805] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 12/01/2022] Open
Abstract
Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman's correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as "virtual biopsy", introducing new insights for omics integration toward a personalized medicine approach.
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Affiliation(s)
| | | | | | - Francesco Isgrò
- Department of Electrical Engineering and Information Technologies, University of Napoli Federico II, Napoli, Italy
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14
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Bobholz SA, Lowman AK, Brehler M, Kyereme F, Duenweg SR, Sherman J, McGarry SD, Cochran EJ, Connelly J, Mueller WM, Agarwal M, Banerjee A, LaViolette PS. Radio-Pathomic Maps of Cell Density Identify Brain Tumor Invasion beyond Traditional MRI-Defined Margins. AJNR Am J Neuroradiol 2022; 43:682-688. [PMID: 35422419 PMCID: PMC9089258 DOI: 10.3174/ajnr.a7477] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 02/07/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Currently, contrast-enhancing margins on T1WI are used to guide treatment of gliomas, yet tumor invasion beyond the contrast-enhancing region is a known confounding factor. Therefore, this study used postmortem tissue samples aligned with clinically acquired MRIs to quantify the relationship between intensity values and cellularity as well as to develop a radio-pathomic model to predict cellularity using MR imaging data. MATERIALS AND METHODS This single-institution study used 93 samples collected at postmortem examination from 44 patients with brain cancer. Tissue samples were processed, stained with H&E, and digitized for nuclei segmentation and cell density calculation. Pre- and postgadolinium contrast T1WI, T2 FLAIR, and ADC images were collected from each patient's final acquisition before death. In-house software was used to align tissue samples to the FLAIR image via manually defined control points. Mixed-effects models were used to assess the relationship between single-image intensity and cellularity for each image. An ensemble learner was trained to predict cellularity using 5 × 5 voxel tiles from each image, with a two-thirds to one-third train-test split for validation. RESULTS Single-image analyses found subtle associations between image intensity and cellularity, with a less pronounced relationship in patients with glioblastoma. The radio-pathomic model accurately predicted cellularity in the test set (root mean squared error = 1015 cells/mm2) and identified regions of hypercellularity beyond the contrast-enhancing region. CONCLUSIONS A radio-pathomic model for cellularity trained with tissue samples acquired at postmortem examination is able to identify regions of hypercellular tumor beyond traditional imaging signatures.
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Affiliation(s)
- S A Bobholz
- From the Departments of Biophysics (S.A.B., S.R.D., J.S., S.D.M.)
| | | | - M Brehler
- Radiology (A.L., M.B., M.A., P.S.L.)
| | | | - S R Duenweg
- From the Departments of Biophysics (S.A.B., S.R.D., J.S., S.D.M.)
| | - J Sherman
- From the Departments of Biophysics (S.A.B., S.R.D., J.S., S.D.M.)
| | - S D McGarry
- From the Departments of Biophysics (S.A.B., S.R.D., J.S., S.D.M.)
| | | | | | | | - M Agarwal
- Radiology (A.L., M.B., M.A., P.S.L.)
| | | | - P S LaViolette
- Radiology (A.L., M.B., M.A., P.S.L.)
- Biomedical Engineering (P.S.L.), Medical College of Wisconsin, Milwaukee, Wisconsin
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15
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Diffusion Restriction Comparison between Gleason 4 Fused Glands and Cribriform Glands within Patient Using Whole-Mount Prostate Pathology as Ground Truth. Tomography 2022; 8:635-643. [PMID: 35314630 PMCID: PMC8938782 DOI: 10.3390/tomography8020053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/13/2022] [Accepted: 02/28/2022] [Indexed: 12/16/2022] Open
Abstract
The presence and extent of cribriform patterned Gleason 4 (G4) glands are associated with poor prognosis following radical prostatectomy. This study used whole-mount prostate histology and multiparametric magnetic resonance imaging (MP-MRI) to evaluate diffusion differences in G4 gland morphology. Fourty-eight patients underwent MP-MRI prior to prostatectomy, of whom 22 patients had regions of both G4 cribriform glands and G4 fused glands (G4CG and G4FG, respectively). After surgery, the prostate was sliced using custom, patient-specific 3D-printed slicing jigs modeled according to the T2-weighted MR image, processed, and embedded in paraffin. Whole-mount hematoxylin and eosin-stained slides were annotated by our urologic pathologist and digitally contoured to differentiate the lumen, epithelium, and stroma. Digitized slides were co-registered to the T2-weighted MRI scan. Linear mixed models were fitted to the MP-MRI data to consider the different hierarchical structures at the patient and slide level. We found that Gleason 4 cribriform glands were more diffusion-restricted than fused glands.
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16
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Hagiwara A, Tatekawa H, Yao J, Raymond C, Everson R, Patel K, Mareninov S, Yong WH, Salamon N, Pope WB, Nghiemphu PL, Liau LM, Cloughesy TF, Ellingson BM. Visualization of tumor heterogeneity and prediction of isocitrate dehydrogenase mutation status for human gliomas using multiparametric physiologic and metabolic MRI. Sci Rep 2022; 12:1078. [PMID: 35058510 PMCID: PMC8776874 DOI: 10.1038/s41598-022-05077-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 01/19/2023] Open
Abstract
This study aimed to differentiate isocitrate dehydrogenase (IDH) mutation status with the voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and to discover biological underpinnings of the clusters. A total of 69 patients with treatment-naïve diffuse glioma were scanned with pH-sensitive amine chemical exchange saturation transfer MRI, diffusion-weighted imaging, fluid-attenuated inversion recovery, and contrast-enhanced T1-weighted imaging at 3 T. An unsupervised two-level clustering approach was used for feature extraction from acquired images. The logarithmic ratio of the labels in each class within tumor regions was applied to a support vector machine to differentiate IDH status. The highest performance to predict IDH mutation status was found for 10-class clustering, with a mean area under the curve, accuracy, sensitivity, and specificity of 0.94, 0.91, 0.90, and 0.91, respectively. Targeted biopsies revealed that the tissues with labels 7-10 showed high expression levels of hypoxia-inducible factor 1-alpha, glucose transporter 3, and hexokinase 2, which are typical of IDH wild-type glioma, whereas those with labels 1 showed low expression of these proteins. In conclusion, A machine learning model successfully predicted the IDH mutation status of gliomas, and the resulting clusters properly reflected the metabolic status of the tumors.
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Affiliation(s)
- Akifumi Hagiwara
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.258269.20000 0004 1762 2738Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Hiroyuki Tatekawa
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.261445.00000 0001 1009 6411Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Jingwen Yao
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA USA
| | - Catalina Raymond
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Richard Everson
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Kunal Patel
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Sergey Mareninov
- grid.19006.3e0000 0000 9632 6718Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - William H. Yong
- grid.19006.3e0000 0000 9632 6718Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Noriko Salamon
- grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Whitney B. Pope
- grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Phioanh L. Nghiemphu
- grid.19006.3e0000 0000 9632 6718UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Linda M. Liau
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Timothy F. Cloughesy
- grid.19006.3e0000 0000 9632 6718UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Benjamin M. Ellingson
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
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Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study. Tomography 2021; 7:866-876. [PMID: 34941645 PMCID: PMC8706942 DOI: 10.3390/tomography7040073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/20/2021] [Accepted: 11/29/2021] [Indexed: 11/17/2022] Open
Abstract
We aimed to evaluate the stability of radiomic features in the liver of healthy individuals across different three-dimensional regions of interest (3D ROI) sizes in T1-weighted (T1w) and T2-weighted (T2w) images from different MR scanners. We retrospectively included 66 examinations of patients without known diseases or pathological imaging findings acquired on three MRI scanners (3 Tesla I: 25 patients, 3 Tesla II: 19 patients, 1.5 Tesla: 22 patients). 3D ROIs of different diameters (10, 20, 30 mm) were drawn on T1w GRE and T2w TSE images into the liver parenchyma (segment V–VIII). We extracted 93 radiomic features from the different ROIs and tested features for significant differences with the Mann–Whitney-U (MWU)-test. The MWU-test revealed significant differences for most second- and higher-order features, indicating a systematic difference dependent on the ROI size. The features mean, median, root mean squared (RMS), 10th percentile, and 90th percentile were not significantly different. We also assessed feature robustness to ROI size variation with overall concordance correlation coefficients (OCCCs). OCCCs across the different ROI-sizes for mean, median, and RMS were excellent (>0.90) in both sequences on all three scanners. These features, therefore, seem robust to ROI-size variation and suitable for radiomic studies of liver MRI.
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Spuhler KD, Teruel JR, Galavis PE. Assessing the reproducibility of CBCT-derived radiomics features using a novel three-dimensional printed phantom. Med Phys 2021; 48:4326-4333. [PMID: 34120354 DOI: 10.1002/mp.15043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 05/04/2021] [Accepted: 06/01/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Radiomics modeling is an exciting avenue for enhancing clinical decision making and personalized treatment. Radiation oncology patients often undergo routine imaging for position verification, particularly using LINAC-mounted cone beam computed tomography (CBCT). The wealth of imaging data collected in modern radiation therapy presents an ideal use case for radiomics modeling. Despite this, texture feature (TF) calculation can be limited by concerns over feature stability and reproducibility; in theory, this issue is compounded by the relatively poor image quality of CBCT, as well as variation of acquisition and reconstruction parameters. METHODS In this study, we developed and validated a novel three-dimensional (3D) printed phantom for evaluating CBCT-based TF reliability. The phantom has a cylindrical shape (22 cm diameter and 25.5 cm height) with five inner inserts designed to hold custom-printed rods (1 cm diameter and 10-20 cm height) of various materials, infill shapes, and densities. TF reproducibility was evaluated across and within three LINACs from a single vendor using sets of three consecutive CBCT taken with the head, thorax, and pelvis clinical imaging protocols. PyRadiomics was used to extract a standard set of TFs from regions of interest centered on each rod. Two-way mixed effects absolute agreement intra-class correlation coefficient (ICC) was used to evaluate TF reproducibility, with features showing ICC values above 0.9 considered robust if their Bonferroni-corrected p-value was below 0.05. RESULTS A total of 63, 87, and 83 features exhibited test-retest reliability for the head, thorax, and pelvis imaging protocols respectively. When assessing stability between discreet imaging sessions on the same LINAC, these numbers were reduced to 5, 63, and 70 features, respectively. The thorax and pelvis protocols maintained a rich candidate feature space in inter-LINAC analysis with 61 and 65 features, respectively, exceeding the ICC criteria. Crucially, no features were deemed reproducible when compared between protocols. CONCLUSIONS We have developed a 3D phantom for consistent evaluation of TF stability and reproducibility. For LINACs from a single vendor, our study found a substantial number of features available for robust radiomics modeling from CBCT imaging. However, some features showed variations across LINACs. Studies involving CBCT-based radiomics must preselect features prior to their use in clinical-based models.
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Affiliation(s)
- Karl D Spuhler
- Department of Radiation Oncology, NYU Langone Medical Center, New York, NY, USA
| | - Jose R Teruel
- Department of Radiation Oncology, NYU Langone Medical Center, New York, NY, USA
| | - Paulina E Galavis
- Department of Radiation Oncology, NYU Langone Medical Center, New York, NY, USA
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Jensen LJ, Kim D, Elgeti T, Steffen IG, Hamm B, Nagel SN. Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study. ACTA ACUST UNITED AC 2021; 7:238-252. [PMID: 34201012 PMCID: PMC8293351 DOI: 10.3390/tomography7020022] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/19/2021] [Accepted: 06/03/2021] [Indexed: 02/01/2023]
Abstract
We aimed to evaluate radiomic features' stability across different region of interest (ROI) sizes in CT and MR images. We chose a phantom with a homogenous internal structure so no differences for a feature extracted from ROIs of different sizes would be expected. For this, we scanned a plastic cup filled with sodium chloride solution ten times in CT and per MR sequence (T1-weighted-gradient-echo and T2-weighted-turbo-inversion-recovery-magnitude). We placed sphere-shaped ROIs of different diameters (4, 8, and 16 mm, and 4, 8, and 16 pixels) into the phantom's center. Features were extracted using PyRadiomics. We assessed feature stability across ROI sizes with overall concordance correlation coefficients (OCCCs). Differences were tested for significance with the Mann-Whitney U-test. Of 93 features, 87 T1w-derived, 87 TIRM-derived, and 70 CT-derived features were significantly different between ROI sizes. Among MR-derived features, OCCCs showed excellent (>0.90) agreement for mean, median, and root mean squared for ROI sizes between 4 and 16 mm and pixels. We further observed excellent agreement for 10th and 90th percentile in T1w and 10th percentile in T2w TIRM images. There was no excellent agreement among the OCCCs of CT-derived features. In summary, many features indicated significant differences and only few showed excellent agreement across varying ROI sizes, although we examined a homogenous phantom. Since we considered a small phantom in an experimental setting, further studies to investigate this size effect would be necessary for a generalization. Nevertheless, we believe knowledge about this effect is crucial in interpreting radiomics studies, as features that supposedly discriminate disease entities may only indicate a systematic difference in ROI size.
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Tomaszewski MR, Gillies RJ. The Biological Meaning of Radiomic Features. Radiology 2021; 298:505-516. [PMID: 33399513 PMCID: PMC7924519 DOI: 10.1148/radiol.2021202553] [Citation(s) in RCA: 226] [Impact Index Per Article: 75.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/30/2020] [Accepted: 08/17/2020] [Indexed: 02/06/2023]
Abstract
An earlier incorrect version appeared online. This article was corrected on February 10, 2021.
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Affiliation(s)
- Michal R. Tomaszewski
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
| | - Robert J. Gillies
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
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Park YW, Choi D, Park M, Ahn SJ, Ahn SS, Suh SH, Lee SK. Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging. J Alzheimers Dis 2021; 79:483-491. [PMID: 33337361 DOI: 10.3233/jad-200734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients. OBJECTIVE To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ42 status when integrated with clinical profiles. METHODS A total of 407 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampus were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated. RESULTS The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set. CONCLUSION Radiomics models from MRI can help predict CSF Aβ42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Dongmin Choi
- Department of Computer Science, Yonsei University, Seoul, Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Jun Ahn
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sang Hyun Suh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
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Alvarez-Jimenez C, Sandino AA, Prasanna P, Gupta A, Viswanath SE, Romero E. Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results. Cancers (Basel) 2020; 12:cancers12123663. [PMID: 33297357 PMCID: PMC7762258 DOI: 10.3390/cancers12123663] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/12/2020] [Accepted: 11/13/2020] [Indexed: 12/18/2022] Open
Abstract
(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.
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Affiliation(s)
- Charlems Alvarez-Jimenez
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Alvaro A. Sandino
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA;
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA;
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Eduardo Romero
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
- Correspondence:
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Abstract
The National Cancer Institute's Quantitative Imaging Network (QIN) has thrived over the past 12 years with an emphasis on the development of image-based decision support software tools for improving measurements of imaging metrics. An overarching goal has been to develop advanced tools that could be translated into clinical trials to provide for improved prediction of response to therapeutic interventions. This article provides an overview of the successes in development and translation of new algorithms into the clinical workflow by the many research teams of the Quantitative Imaging Network.
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