Evidence map›Paper›PMID 35809210›Full record

ArticleJapanese journal of radiology2022

Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images.

Ying-Hwey Nai, Hoi Yin Loi, Sophie O'Doherty, Teng Hwee Tan, Anthonin Reilhac

Abstract read
PubMed Publisher
In one paragraph

Article in Japanese journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
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

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
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  8. Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023
    Review
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

5 authors at 3 institutions in 1 country.

Ying-Hwey NaiClinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore. mednyh@nus.edu.sg.ORCID http://orcid.org/0000-0002-6634-8785
Hoi Yin LoiDepartment of Diagnostic Imaging, National University Hospital, Singapore, Singapore.
Sophie O'DohertyClinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore.
Teng Hwee TanDepartment of Radiation Oncology, National University Cancer Institute, Singapore, Singapore.ORCID http://orcid.org/0000-0003-0649-9714
Anthonin ReilhacClinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore.ORCID http://orcid.org/0000-0002-9997-9540
National University of Singapore · SGNational University Cancer Institute, Singapore · SGNational University Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo compare the performances of machine learning (ML) and deep learning (DL) in improving the quality of low dose (LD) lung cancer PET images and the minimum counts required. MATERIALS AND

methods33 standard dose (SD) PET images, were used to simulate LD PET images at seven-count levels of 0.25, 0.5, 1, 2, 5, 7.5 and 10 million (M) counts. Image quality transfer (IQT), a ML algorithm that uses decision tree and patch-sampling was compared to two DL networks-HighResNet (HRN) and deep-boosted regression (DBR). Supervised training was performed by training the ML and DL algorithms with matched-pair SD and LD images. Image quality evaluation and clinical lesion detection tasks were performed by three readers. Bias in 53 radiomic features, including mean SUV, was evaluated for all lesions.

resultsML- and DL-estimated images showed higher signal and smaller error than LD images with optimal image quality recovery achieved using LD down to 5 M counts. True positive rate and false discovery rate were fairly stable beyond 5 M counts for the detection of small and large true lesions. Readers rated average or higher ratings to images estimated from LD images of count levels above 5 M only, with higher confidence in detecting true lesions.

conclusionLD images with a minimum of 5 M counts (8.72 MBq for 10 min scan or 25 MBq for 3 min scan) are required for optimal clinical use of ML and DL, with slightly better but more varied performance shown by DL.

Indexed as

Deep LearningLung NeoplasmsAlgorithmsHumansImage Processing, Computer-AssistedMachine LearningPositron-Emission TomographyDeep learningImage qualityLesion detectionLow-dose lung cancer PETMachine learning

Identifiers

PMID35809210
OpenAlexW4284988499

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.