Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning

Marc J. Pomeroy; Yongfeng Gao; Weiguo Cao; Perry J. Pickhardt; Zhengrong Liang

doi:10.1109/NSS/MIC44845.2022.10398893

Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning

Marc J. Pomeroy
, Yongfeng Gao
, Weiguo Cao
, Perry J. Pickhardt
, Zhengrong Liang

Radiology

Stony Brook University
University of Wisconsin-Madison

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Computed tomographic colonography (CTC) has shown to be an effective means of detecting the precursor polyps leading to colorectal cancer (CRC). Accurately diagnosing these polyps can assist the overall treatment plan and follow-up examination determination to prevent CRC. In this work, we examine a machine learning approach to predicting the three main pathological structure type of polyps, comparing hyperplastic and serrated adenomas (HS), tubular adenomas (TA), and advanced neoplasms (AN). We find that imaging textures can differentiate any two pathological groups with AUC scores ranging from 0.624-0.749 for polyps less than 10mm in size and 0.731-0.887 for polyps greater than 10mm. Classification of all three pathology groups simultaneously shows a sensitivity of 61.6% for identifying the highest risk group of advanced neoplasms.

Original language	English
Title of host publication	2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665488723
DOIs	https://doi.org/10.1109/NSS/MIC44845.2022.10398893
State	Published - 2022
Event	2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022 - Milano, Italy Duration: Nov 5 2022 → Nov 12 2022

Publication series

Name	2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference

Conference

Conference	2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022
Country/Territory	Italy
City	Milano
Period	11/5/22 → 11/12/22

Access to Document

10.1109/NSS/MIC44845.2022.10398893

Cite this

Pomeroy, M. J., Gao, Y., Cao, W., Pickhardt, P. J., & Liang, Z. (2022). Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning. In 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference (2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NSS/MIC44845.2022.10398893

Pomeroy, Marc J. ; Gao, Yongfeng ; Cao, Weiguo et al. / Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning. 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference).

@inproceedings{33537b44f774451398d5b8c1eb5065f3,

title = "Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning",

abstract = "Computed tomographic colonography (CTC) has shown to be an effective means of detecting the precursor polyps leading to colorectal cancer (CRC). Accurately diagnosing these polyps can assist the overall treatment plan and follow-up examination determination to prevent CRC. In this work, we examine a machine learning approach to predicting the three main pathological structure type of polyps, comparing hyperplastic and serrated adenomas (HS), tubular adenomas (TA), and advanced neoplasms (AN). We find that imaging textures can differentiate any two pathological groups with AUC scores ranging from 0.624-0.749 for polyps less than 10mm in size and 0.731-0.887 for polyps greater than 10mm. Classification of all three pathology groups simultaneously shows a sensitivity of 61.6\% for identifying the highest risk group of advanced neoplasms.",

author = "Pomeroy, \{Marc J.\} and Yongfeng Gao and Weiguo Cao and Pickhardt, \{Perry J.\} and Zhengrong Liang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022 ; Conference date: 05-11-2022 Through 12-11-2022",

year = "2022",

doi = "10.1109/NSS/MIC44845.2022.10398893",

language = "English",

series = "2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference",

}

Pomeroy, MJ, Gao, Y, Cao, W, Pickhardt, PJ & Liang, Z 2022, Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning. in 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference. 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022, Milano, Italy, 11/5/22. https://doi.org/10.1109/NSS/MIC44845.2022.10398893

Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning. / Pomeroy, Marc J.; Gao, Yongfeng; Cao, Weiguo et al.
2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning

AU - Pomeroy, Marc J.

AU - Gao, Yongfeng

AU - Cao, Weiguo

AU - Pickhardt, Perry J.

AU - Liang, Zhengrong

PY - 2022

Y1 - 2022

N2 - Computed tomographic colonography (CTC) has shown to be an effective means of detecting the precursor polyps leading to colorectal cancer (CRC). Accurately diagnosing these polyps can assist the overall treatment plan and follow-up examination determination to prevent CRC. In this work, we examine a machine learning approach to predicting the three main pathological structure type of polyps, comparing hyperplastic and serrated adenomas (HS), tubular adenomas (TA), and advanced neoplasms (AN). We find that imaging textures can differentiate any two pathological groups with AUC scores ranging from 0.624-0.749 for polyps less than 10mm in size and 0.731-0.887 for polyps greater than 10mm. Classification of all three pathology groups simultaneously shows a sensitivity of 61.6% for identifying the highest risk group of advanced neoplasms.

AB - Computed tomographic colonography (CTC) has shown to be an effective means of detecting the precursor polyps leading to colorectal cancer (CRC). Accurately diagnosing these polyps can assist the overall treatment plan and follow-up examination determination to prevent CRC. In this work, we examine a machine learning approach to predicting the three main pathological structure type of polyps, comparing hyperplastic and serrated adenomas (HS), tubular adenomas (TA), and advanced neoplasms (AN). We find that imaging textures can differentiate any two pathological groups with AUC scores ranging from 0.624-0.749 for polyps less than 10mm in size and 0.731-0.887 for polyps greater than 10mm. Classification of all three pathology groups simultaneously shows a sensitivity of 61.6% for identifying the highest risk group of advanced neoplasms.

UR - https://www.scopus.com/pages/publications/85185385039

U2 - 10.1109/NSS/MIC44845.2022.10398893

DO - 10.1109/NSS/MIC44845.2022.10398893

M3 - Conference contribution

AN - SCOPUS:85185385039

T3 - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference

BT - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

Y2 - 5 November 2022 through 12 November 2022

ER -

Pomeroy MJ, Gao Y, Cao W, Pickhardt PJ, Liang Z. Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning. In 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference). doi: 10.1109/NSS/MIC44845.2022.10398893

Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning

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