Evidence map›Paper›PMID 39857996›Full record

ArticleCancers2025

XGBoost Enhances the Performance of SAFE: A Novel Microwave Imaging System for Early Detection of Malignant Breast Cancer.

Ali Yurtseven, Aleksandar Janjic, Mehmet Cayoren, Onur Bugdayci, Mustafa Erkin Aribal, Ibrahim Akduman

Abstract read
In one paragraph

Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. AI in Medical Imaging and Image Processing.Journal of clinical medicine · 2025
    Article
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.

Ali YurtsevenMitos Medical Technologies, ITU Ayazaga Ari 1, Maslak, 34469 Istanbul, Turkey.
Aleksandar JanjicMitos Medical Technologies, ITU Ayazaga Ari 1, Maslak, 34469 Istanbul, Turkey.
Mehmet CayorenMitos Medical Technologies, ITU Ayazaga Ari 1, Maslak, 34469 Istanbul, Turkey.
Onur BugdayciDepartment of Radiology, School of Medicine, Marmara University, Pendik, 34899 Istanbul, Turkey.ORCID 0000-0002-6011-2720
Mustafa Erkin AribalMitos Medical Technologies, ITU Ayazaga Ari 1, Maslak, 34469 Istanbul, Turkey.ORCID 0000-0002-5525-8696
Ibrahim AkdumanMitos Medical Technologies, ITU Ayazaga Ari 1, Maslak, 34469 Istanbul, Turkey.ORCID 0000-0002-5769-6032

Funding

Scientific and Technology Research Council of Turkey 244 (TUBITAK) 244
6 · The paper itself

Abstract

BACKGROUND/

objectivesBreast cancer is a significant global health concern, and early detection is crucial for improving patient outcomes. Mammography is widely used but has limitations, particularly for younger women with denser breasts. These include reduced sensitivity, false positives, and radiation risks. This highlights the need for alternative screening methods. In this study, we assess the performance of SAFE (Scan and Find Early), a novel microwave imaging device, in detecting breast cancer in a larger patient cohort. Unlike previous studies that predominantly relied on cross-validation, this study employs a more reliable, independent evaluation methodology to enhance generalizability.

methodsWe developed an XGBoost model to classify breast cancer cases into positive (malignant) and negative (benign or healthy) groups. The model was analyzed with respect to key factors such as breast size, density, age, tumor size, and histopathological findings. This approach provides a better understanding of how these factors influence the model's performance, using an independent evaluation methodology for increased reliability.

resultsOur results demonstrate that SAFE exhibits high sensitivity, particularly in dense breasts (91%) and younger patients (83%), suggesting its potential as a supplemental screening tool. Additionally, the system shows high detection accuracy for both small (<2 cm) and larger lesions, proving effective in early cancer detection.

conclusionsThis study reinforces the potential of SAFE to complement existing screening methods, particularly for patients with dense breasts, where mammography's sensitivity is reduced. The promising results warrant further research to solidify SAFE's clinical application as an alternative screening tool for breast cancer detection.

Indexed as

breast cancer detectionmachine learningmedical imagingmicrowave imagingSAFEtumor classification

Identifiers

PMID39857996
PMCPMC11764354

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Registered trials

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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.