Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in breath samples were evaluated in a double-blind study. Breath samples of 1386 participants (59.7% males, median age 56.0 years) who underwent screening for cancer using gold-standard screening methods, or a biopsy for a suspected malignancy were collected. The samples were analyzed using a bio-hybrid platform comprising trained detection canines and artificial intelligence tools. According to cancer screening/biopsy results, 1048 (75.6%) were negative for cancer and 338 (24.4%) were positive. Among the 338 positive samples, 261 (77.2%) were positive for one of the four cancer types that the bio-hybrid platform was trained to detect, with an overall sensitivity and specificity of 93.9% (95% confidence interval [CI] 90.3-96.2%) and 94.3% (95% CI 92.7%-95.5%), respectively. The sensitivity of each cancer type was similar; breast: 95.0% (95% CI 87.8-98.0%), lung: 95.0% (95% CI 87.8-98.0%), colorectal: 90.0% (95% CI 74.4-96.5%), prostate: 93.0% (95% CI 84.6-97.0%). The sensitivity of 14 other malignant tumors that the bio-hybrid platform was not trained to detect, but identified, was 81.8% (95% CI 71.8%-88.8%). Early cancer (0-2) detection sensitivity was 94.8% (95% CI 91.0%-97.1%). This bio-hybrid multi-cancer screening platform demonstrated high sensitivity and specificity and enables early-stage cancer detection.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2024-11-15
DOI
https://doi.org/10.1038/s41598-024-79383-2
Citations
6
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
1.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study

Nadir Arber, Assaf Rabinowicz, Elizabeth Half, Adelina Ovcharenko et al.
6 citations
Scientific Reports
Advanced Chemical Sensor Technologies
1.11
article

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study

Nadir Arber, Assaf Rabinowicz, Elizabeth Half, Adelina Ovcharenko, Ronit Shmuel, Sharon Furman-Assaf, Milana Avdalimov
article en
6 citations

Abstract

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in breath samples were evaluated in a double-blind study. Breath samples of 1386 participants (59.7% males, median age 56.0 years) who underwent screening for cancer using gold-standard screening methods, or a biopsy for a suspected malignancy were collected. The samples were analyzed using a bio-hybrid platform comprising trained detection canines and artificial intelligence tools. According to cancer screening/biopsy results, 1048 (75.6%) were negative for cancer and 338 (24.4%) were positive. Among the 338 positive samples, 261 (77.2%) were positive for one of the four cancer types that the bio-hybrid platform was trained to detect, with an overall sensitivity and specificity of 93.9% (95% confidence interval [CI] 90.3-96.2%) and 94.3% (95% CI 92.7%-95.5%), respectively. The sensitivity of each cancer type was similar; breast: 95.0% (95% CI 87.8-98.0%), lung: 95.0% (95% CI 87.8-98.0%), colorectal: 90.0% (95% CI 74.4-96.5%), prostate: 93.0% (95% CI 84.6-97.0%). The sensitivity of 14 other malignant tumors that the bio-hybrid platform was not trained to detect, but identified, was 81.8% (95% CI 71.8%-88.8%). Early cancer (0-2) detection sensitivity was 94.8% (95% CI 91.0%-97.1%). This bio-hybrid multi-cancer screening platform demonstrated high sensitivity and specificity and enables early-stage cancer detection.

Scientific ReportsVol. 14(1)
Tel Aviv University (IL), Tel Aviv Sourasky Medical Center (IL), Rambam Health Care Campus (IL), Israel Dairy Board (Israel) (IL)
Good health and well-being
Openalex Percentile: Top 24%
Advanced Chemical Sensor Technologies
1.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.