Evidence map›Paper›PMID 40760396›Full record

ArticlePhysical and engineering sciences in medicine2025

Enhanced detection of ovarian cancer using AI-optimized 3D CNNs for PET/CT scan analysis.

Mohammad Hossein Sadeghi, Sedigheh Sina, Reza Faghihi, Mehrosadat Alavi, Francesco Giammarile, Hamid Omidi

Abstract read
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In one paragraph

Article in Physical and engineering sciences in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Mohammad Hossein SadeghiNuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
Sedigheh SinaNuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran. samirasina@shirazu.ac.ir.
Reza FaghihiNuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
Mehrosadat AlaviIonizing and Non-Ionizing Radiation Protection Research Center, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
Francesco GiammarileNuclear Medicine and Diagnostic Imaging Section, Division of Human Health, International Atomic Energy Agency, Vienna, Austria.
Hamid OmidiNuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran.

Funding

International Atomic Energy Agency CRP - E13050
6 · The paper itself

Abstract

This study investigates how deep learning (DL) can enhance ovarian cancer diagnosis and staging using large imaging datasets. Specifically, we compare six conventional convolutional neural network (CNN) architectures-ResNet, DenseNet, GoogLeNet, U-Net, VGG, and AlexNet-with OCDA-Net, an enhanced model designed for [

Indexed as

Imaging, Three-DimensionalNeural Networks, ComputerOvarian NeoplasmsPositron Emission Tomography Computed TomographyDeep LearningFemaleFluorodeoxyglucose F18HumansNeoplasm StagingFluorodeoxyglucose F18Artificial intelligenceDeep learningOvarian cancerPET/CT

Identifiers

PMID40760396

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.