Evidence map›Paper›PMID 32550599›Full record

ArticleRadiology. Imaging cancer2020

CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma.

Pritam Mukherjee, Murilo Cintra, Chao Huang, Mu Zhou, Shankuan Zhu, A Dimitrios Colevas, Nancy Fischbein, Olivier Gevaert

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Imaging cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed.

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  9. Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning.Journal of imaging informatics in medicine · 2024
    Article
  10. Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Pritam MukherjeeDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479.ORCID 0000-0002-9975-9994
Murilo CintraDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479.ORCID 0000-0002-9862-0392
Chao HuangDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479.ORCID 0000-0003-4974-2649
Mu ZhouDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479.
Shankuan ZhuDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479.
A Dimitrios ColevasDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479.
Nancy FischbeinDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479.
Olivier GevaertDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479.ORCID 0000-0002-9965-5466

Funding

Radiogenomics Framework for Non-Invasive Personalized MedicineR01EB020527 · NIBIB · STANFORD UNIVERSITY · PI GEVAERT, OLIVIER · 2015 to 2018
$2.3M
Radiogenomics framework for non-invasive personalized medicineR56EB020527 · NIBIB · STANFORD UNIVERSITY · PI GEVAERT, OLIVIER · 2019 to 2019
$444k
NIBIB NIH HHS R01 EB020527NIBIB NIH HHS R56 EB020527
6 · The paper itself

Abstract

Purpose: To determine the performance of CT-based radiomic features for noninvasive prediction of histopathologic features of tumor grade, extracapsular spread, perineural invasion, lymphovascular invasion, and human papillomavirus status in head and neck squamous cell carcinoma (HNSCC). Materials and Methods: In this retrospective study, which was approved by the local institutional ethics committee, CT images and clinical data from patients with pathologically proven HNSCC from The Cancer Genome Atlas ( Results: The trained radiomic model demonstrated moderate capability of predicting HNSCC features. In the training cohort and the test cohort, the model achieved a mean area under the receiver operating characteristic curve (AUC) of 0.75 (95% confidence interval [CI]: 0.68, 0.81) and 0.66 (95% CI: 0.45, 0.84), respectively, for tumor grade; a mean AUC of 0.64 (95% CI: 0.55, 0.62) and 0.70 (95% CI: 0.47, 0.89), respectively, for perineural invasion; a mean AUC of 0.69 (95% CI: 0.56, 0.81) and 0.65 (95% CI: 0.38, 0.87), respectively, for lymphovascular invasion; a mean AUC of 0.77 (95% CI: 0.65, 0.88) and 0.67 (95% CI: 0.15, 0.80), respectively, for extracapsular spread; and a mean AUC of 0.71 (95% CI: 0.29, 1.0) and 0.80 (95% CI: 0.65, 0.92), respectively, for human papillomavirus status. Conclusion: Radiomic CT models have the potential to predict characteristics typically identified on pathologic assessment of HNSCC.

Indexed as

Tomography, X-Ray ComputedHumansPapillomaviridaeRetrospective StudiesSquamous Cell Carcinoma of Head and Neck

Identifiers

PMID32550599
PMCPMC7263288

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