Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064203%3A_____%2F23%3A10458480" target="_blank" >RIV/00064203:_____/23:10458480 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/23:10458480 RIV/00209805:_____/23:00079208

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=DEq.3Pva3h" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=DEq.3Pva3h</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/diagnostics13061043" target="_blank" >10.3390/diagnostics13061043</a>

Result language

angličtina

Original language name

Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images

Original language description

Chest X-ray (CXR) is considered to be the most widely used modality for detecting and monitoring various thoracic findings, including lung carcinoma and other pulmonary lesions. However, X-ray imaging shows particular limitations when detecting primary and secondary tumors and is prone to reading errors due to limited resolution and disagreement between radiologists. To address these issues, we developed a deep-learning-based automatic detection algorithm (DLAD) to automatically detect and localize suspicious lesions on CXRs. Five radiologists were invited to retrospectively evaluate 300 CXR images from a specialized oncology center, and the performance of individual radiologists was subsequently compared with that of DLAD. The proposed DLAD achieved significantly higher sensitivity (0.910 (0.854-0.966)) than that of all assessed radiologists (RAD 10.290 (0.201-0.379), p &lt; 0.001, RAD 20.450 (0.352-0.548), p &lt; 0.001, RAD 30.670 (0.578-0.762), p &lt; 0.001, RAD 40.810 (0.733-0.887), p = 0.025, RAD 50.700 (0.610-0.790), p &lt; 0.001). The DLAD specificity (0.775 (0.717-0.833)) was significantly lower than for all assessed radiologists (RAD 11.000 (0.984-1.000), p &lt; 0.001, RAD 20.970 (0.946-1.000), p &lt; 0.001, RAD 30.980 (0.961-1.000), p &lt; 0.001, RAD 40.975 (0.953-0.997), p &lt; 0.001, RAD 50.995 (0.985-1.000), p &lt; 0.001). The study results demonstrate that the proposed DLAD could be utilized as a decision-support system to reduce radiologists&apos; false negative rate.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30204 - Oncology

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Diagnostics

ISSN

2075-4418

e-ISSN

2075-4418

Volume of the periodical

13

Issue of the periodical within the volume

6

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

1043

UT code for WoS article

000955949800001

EID of the result in the Scopus database

2-s2.0-85152356552