Evidence map›Paper›PMID 38589677›Full record

ArticleAnnals of nuclear medicine2024

Diagnostic performance of a deep-learning model using

Changhwan Sung, Jungsu S Oh, Byung Soo Park, Su Ssan Kim, Si Yeol Song, Jong Jin Lee

Erratum issuedAbstract read
PubMed Publisher
In one paragraph

Article in Annals of nuclear medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Changhwan Sung *Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Jungsu S Oh *Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Byung Soo ParkDepartment of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Su Ssan KimDepartment of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Si Yeol SongDepartment of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Jong Jin LeeDepartment of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea. jongjin@gmail.com.ORCID http://orcid.org/0000-0002-3184-2409

Funding

Ministry of Health & Welfare, Republic of Korea HI18C2383National Research Foundation of Korea 2021R1A2C3009056
6 · The paper itself

Abstract

objectiveWe developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.

methodsWe retrospectively recruited 308 patients with lung cancer with RT-related changes observed on

resultsFor the five independent test sets, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity were in the range of 0.98-0.99, 95-98%, and 87-95%, respectively. The region determined by the model was confirmed as an actual recurred tumour through the explainable artificial intelligence (AI) using gradient-weighted class activation mapping (Grad-CAM).

conclusionThe 2D slice-based CNN model using

Indexed as

Deep LearningFluorodeoxyglucose F18Lung NeoplasmsPositron Emission Tomography Computed TomographyAdultAgedAged, 80 and overFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedNeoplasm Recurrence, LocalRecurrenceRetrospective StudiesFluorodeoxyglucose F1818F-FDG PET/CTDeep learningImage processingLung cancerRecurrence prediction

Identifiers

PMID38589677

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.