Evidence map›Paper›PMID 40343586›Full record

ArticleClinical and experimental medicine2025

Binary classification of gynecological cancers based on ATR-FTIR spectroscopy and machine learning using urine samples.

Francesco Vigo, Alessandra Tozzi, Flavio C Lombardo, Muriel Eugster, Vasileios Kavvadias, Rahel Brogle, Julia Rigert, Viola Heinzelmann-Schwarz, Tilemachos Kavvadias

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Article in Clinical and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Francesco Vigo *Department of Biomedicine, University of Basel, Basel, Switzerland.
Alessandra Tozzi *Department of Biomedicine, University of Basel, Basel, Switzerland.
Flavio C Lombardo *Department of Biomedicine, University of Basel, Basel, Switzerland.
Muriel EugsterDepartment of Biomedicine, University of Basel, Basel, Switzerland.
Vasileios KavvadiasUniversity Hospital of Basel, Basel, Switzerland.
Rahel BrogleMedicine, University of Basel, Basel, Switzerland.
Julia RigertMedicine, University of Basel, Basel, Switzerland.
Viola Heinzelmann-SchwarzDepartment of Biomedicine, University of Basel, Basel, Switzerland.
Tilemachos KavvadiasDepartment of Gynecology, Clinic for Gynecology and Gynecologic Oncology, University Hospital of Basel, Spitalstrasse 21, 4055, Basel, Switzerland. tilemachos.kavvadias@usb.ch.

Funding

Krebsliga Beider Basel KLbB-5068-02-2020Medical and Surgical Society of Cordu 27/11-10-2023
6 · The paper itself

Abstract

Making an early diagnosis of cancer still in the early stages, when completely asymptomatic, is the challenge modern medicine has been setting for several decades. In gynecology, no effective screening has yet been found and approved for endometrial and ovarian cancer. Mammography is an effective screening method for Breast Cancer, as well as Pap Test for Cervical Cancer, but they are underused in third world countries because of their expensive and specific instrumentation. Previous studies showed how "machine learning analysis methods" of the spectral information obtained from dried urine samples could provide good accuracy in differentiation between healthy and ovarian or endometrial cancer. In this study, we also apply ATR-FTIR spectrometry's practical, fast, and relatively inexpensive principles to liquid urine analysis from 309 patients undergoing surgical treatment for benign or malignant diseases (endometrium, breast, cervix, vulvar and ovarian cancer). The data obtained from those liquid samples were then analyzed to train a machine learning model to classify healthy VS cancer patients. We obtained an accuracy of > 91%, and we also identified discriminant wavelengths (2093, 1774 cm

Indexed as

Genital Neoplasms, FemaleMachine LearningAdultAgedEarly Detection of CancerFemaleHumansMiddle AgedSpectroscopy, Fourier Transform InfraredATR-FTIR spectroscopyGynecological cancersMachine learningUrine biomarkers

Identifiers

PMID40343586
PMCPMC12064457

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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.