Relationship of TTF-1 and EGFR on Lung Adenocarcinoma at Dr. Saiful Anwar General Hospital Malang

gaol, andy lumban; Pratiwi, Suryanti Dwi; Putra, Ngakan Putu Parsama; Norahmawati, Eviana; Rasyid, Harun Al

doi:10.21776/ub.jkb.2020.031.01.9

JKB

2020

DOI: 10.21776/ub.jkb.2020.031.01.9

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Relationship of TTF-1 and EGFR on Lung Adenocarcinoma at Dr. Saiful Anwar General Hospital Malang

andy lumban gaol

Suryanti Dwi Pratiwi

Ngakan Putu Parsama Putra

et al.

Abstract: <p>There is a correlation between mutation of Epidermal Growth Factor Receptor (EGFR) and lung adenocarcinoma. Unfortunately, examination for EGFR mutation is difficult because surgery must be conducted to obtain the best specimen. Thyroid Transcription Factor-1 (TTF-1) is a marker for lung adenocarcinoma. This observational study took place at Dr. Saiful Anwar Hospital from stored biological materials from 2013-2018. Samples were lung adenocarcinoma patients that undergo EGFR examination. Data then anal… Show more

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Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

Lang

et al. 2021

Journal of Healthcare Engineering

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In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnosis due to their cheapness, no radiation, and low cost. The use of image processing technology to accurately segment the nodule area provides important auxiliary information for the doctor’s diagnosis, which is of great value for guiding clinical treatment. The purpose of this article is to explore the application value of combined detection of abnormal sugar-chain glycoprotein (TAP) and carcinoembryonic antigen (CEA) in the risk estimation of thyroid cancer in patients with thyroid nodules of type IV and above based on deep learning medical images. In this paper, ultrasound thyroid images are used as the research content, and the active contour level set method is used as the segmentation basis, and a segmentation algorithm for thyroid nodules is proposed. This paper takes ultrasound thyroid images as the research content, uses the active contour level set method as the basis of segmentation, and proposes an image segmentation algorithm Fast-SegNet based on deep learning, which extends the network model that was mainly used for thyroid medical image segmentation to more scenarios of the segmentation task. From January 2019 to October 2020, 400 patients with thyroid nodules of type IV and above were selected for physical examination and screening at the Health Management Center of our hospital, and they were diagnosed as thyroid cancer by pathological examination of thyroid nodules under B-ultrasound positioning. The detection rates of thyroid cancer in patients with thyroid nodules of type IV and above are compared; serum TAP and CEA levels are detected; PT-PCR is used to detect TTF-1, PTEN, and NIS expression; the detection, missed diagnosis, misdiagnosis rate, and diagnostic efficiency of the three detection methods are compared. This article uses the thyroid nodule region segmented based on deep learning medical images and compares experiments with CV model, LBF model, and DRLSE model. The experimental results show that the segmentation overlap rate of this method is as high as 98.4%, indicating that the algorithm proposed in this paper can more accurately extract the thyroid nodule area.

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Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

Lang

et al. 2021

Journal of Healthcare Engineering

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Relationship of TTF-1 and EGFR on Lung Adenocarcinoma at Dr. Saiful Anwar General Hospital Malang

Cited by 1 publication

References 5 publications

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

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