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Ahmadian SS, Jones D, Wakely P, Lott Limbach AA. Thyroid poorly differentiated carcinoma metastatic to pancreas diagnosed by fine-needle aspiration and demonstrating a novel BRAF fusion. Cytopathology 2024; 35:408-411. [PMID: 38361348 DOI: 10.1111/cyt.13353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/12/2023] [Accepted: 12/19/2023] [Indexed: 02/17/2024]
Abstract
Differentiating pancreatic duct adenocarcinoma from metastasis can be challenging by morphology alone. Metastasis from a classic papillary thyroid carcinoma can present as a poorly differentiated carcinoma and mimic pancreatic ductal adenocarcinoma's morphology.
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Affiliation(s)
| | - Daniel Jones
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | - Paul Wakely
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
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Liu XP, Jin X, Seyed Ahmadian S, Yang X, Tian SF, Cai YX, Chawla K, Snijders AM, Xia Y, van Diest PJ, Weiss WA, Mao JH, Li ZQ, Vogel H, Chang H. Clinical significance and molecular annotation of cellular morphometric subtypes in lower-grade gliomas discovered by machine learning. Neuro Oncol 2022; 25:68-81. [PMID: 35716369 PMCID: PMC9825346 DOI: 10.1093/neuonc/noac154] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Lower-grade gliomas (LGG) are heterogeneous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes. METHODS Cellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5-year overall survival (OS) of LGG patients. Tumor mutational burden (TMB) and immune cell infiltration between subtypes were analyzed using the Mann-Whitney U test. The double-blinded validation for important immunotherapy-related biomarkers was executed using immunohistochemistry (IHC). RESULTS We developed a machine learning (ML) pipeline to extract CMBs from whole-slide images of tissue histology; identifying and externally validating robust CMS of LGGs in multicenter cohorts. The subtypes had independent predicted OS across all three independent cohorts. In the TCGA-LGG cohort, patients within the poor-prognosis subtype responded poorly to primary and follow-up therapies. LGGs within the poor-prognosis subtype were characterized by high mutational burden, high frequencies of copy number alterations, and high levels of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher levels of PD-1/PD-L1/CTLA-4 were confirmed by IHC staining. In addition, the subtypes learned from LGG demonstrate translational impact on glioblastoma (GBM). CONCLUSIONS We developed and validated a framework (CMS-ML) for CMS discovery in LGG associated with specific molecular alterations, immune microenvironment, prognosis, and treatment response.
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Affiliation(s)
| | | | - Saman Seyed Ahmadian
- Department of Pathology, Stanford University Medical Center, Stanford, California, USA
| | - Xu Yang
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA,Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA,Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Su-Fang Tian
- Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yu-Xiang Cai
- Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Kuldeep Chawla
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Antoine M Snijders
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA,Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Yankai Xia
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - William A Weiss
- Departments of Neurology, Neurological Surgery, and Pediatrics, University of California, San Francisco, San Francisco, California, USA
| | - Jian-Hua Mao
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA,Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Zhi-Qiang Li
- Corresponding Authors: Zhi-Qiang Li, MD, PhD, Department of Neurosurgery, Zhongnan Hospital of Wuhan University, 169 East Lake Road, Wuchang District, Wuhan, Hubei 430071 China (); Hang Chang, PhD, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA ()
| | | | - Hang Chang
- Corresponding Authors: Zhi-Qiang Li, MD, PhD, Department of Neurosurgery, Zhongnan Hospital of Wuhan University, 169 East Lake Road, Wuchang District, Wuhan, Hubei 430071 China (); Hang Chang, PhD, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA ()
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Zhang M, Wong SW, Lummus S, Han M, Radmanesh A, Ahmadian SS, Prolo LM, Lai H, Eghbal A, Oztekin O, Cheshier SH, Fisher PG, Ho CY, Vogel H, Vitanza NA, Lober RM, Grant GA, Jaju A, Yeom KW. Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma. AJNR Am J Neuroradiol 2021; 42:1702-1708. [PMID: 34266866 DOI: 10.3174/ajnr.a7200] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 04/05/2021] [Indexed: 01/06/2023]
Abstract
BACKGROUND AND PURPOSE Atypical teratoid/rhabdoid tumors and medulloblastomas have similar imaging and histologic features but distinctly different outcomes. We hypothesized that they could be distinguished by MR imaging-based radiomic phenotypes. MATERIALS AND METHODS We retrospectively assembled T2-weighted and gadolinium-enhanced T1-weighted images of 48 posterior fossa atypical teratoid/rhabdoid tumors and 96 match-paired medulloblastomas from 7 institutions. Using a holdout test set, we measured the performance of 6 candidate classifier models using 6 imaging features derived by sparse regression of 900 T2WI and 900 T1WI Imaging Biomarker Standardization Initiative-based radiomics features. RESULTS From the originally extracted 1800 total Imaging Biomarker Standardization Initiative-based features, sparse regression consistently reduced the feature set to 1 from T1WI and 5 from T2WI. Among classifier models, logistic regression performed with the highest AUC of 0.86, with sensitivity, specificity, accuracy, and F1 scores of 0.80, 0.82, 0.81, and 0.85, respectively. The top 3 important Imaging Biomarker Standardization Initiative features, by decreasing order of relative contribution, included voxel intensity at the 90th percentile, inverse difference moment normalized, and kurtosis-all from T2WI. CONCLUSIONS Six quantitative signatures of image intensity, texture, and morphology distinguish atypical teratoid/rhabdoid tumors from medulloblastomas with high prediction performance across different machine learning strategies. Use of this technique for preoperative diagnosis of atypical teratoid/rhabdoid tumors could significantly inform therapeutic strategies and patient care discussions.
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Affiliation(s)
- M Zhang
- From the Departments of Neurosurgery (M.Z.)
| | - S W Wong
- Department of Statistics (S.W.W.), Stanford University, Stanford, California
| | - S Lummus
- Department of Physiology and Nutrition (S.L.), University of Colorado, Colorado Springs, Colorado
| | - M Han
- Department of Pediatrics (M.H.), Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - A Radmanesh
- Department of Radiology (A.R.), New York University Grossman School of Medicine, New York, New York
| | - S S Ahmadian
- Pathology (S.S.A., H.V.), Stanford Medical Center, Stanford University, Stanford, California
| | - L M Prolo
- Departments of Neurosurgery (L.M.P., G.A.G.)
| | - H Lai
- Department of Radiology (H.L., A.E.), Children's Hospital of Orange County, Orange, California and University of California, Irvine, Irvine, California
| | - A Eghbal
- Department of Radiology (H.L., A.E.), Children's Hospital of Orange County, Orange, California and University of California, Irvine, Irvine, California
| | - O Oztekin
- Department of Neuroradiology (O.O.), Cigli Education and Research Hospital, Bakircay University, Izmir, Turkey.,Department of Neuroradiology (O.O.), Tepecik Education and Research Hospital, Health Science University, Izmir, Turkey
| | - S H Cheshier
- Division of Pediatric Neurosurgery (S.H.C.), Department of Neurosurgery, Huntsman Cancer Institute, Intermountain Healthcare Primary Children's Hospital, University of Utah School of Medicine, Salt Lake City, Utah
| | | | - C Y Ho
- Departments of Clinical Radiology & Imaging Sciences (C.Y.H.), Riley Children's Hospital, Indiana University, Indianapolis, Indiana
| | - H Vogel
- Pathology (S.S.A., H.V.), Stanford Medical Center, Stanford University, Stanford, California
| | - N A Vitanza
- Division of Pediatric Hematology/Oncology (N.A.V.), Department of Pediatrics, Seattle Children's Hospital, Seattle, Washington
| | - R M Lober
- Division of Neurosurgery (R.M.L.), Department of Pediatrics, Wright State University Boonshoft School of Medicine, Dayton Children's Hospital, Dayton, Ohio
| | - G A Grant
- Departments of Neurosurgery (L.M.P., G.A.G.)
| | - A Jaju
- Department of Medical Imaging (A.J.), Ann and Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - K W Yeom
- Radiology (K.W.Y.), Lucile Packard Children's Hospital, Stanford University, Palo Alto, California
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