Clinical trial · Observational
Intraoperative Detection of Breast Cancer by Electrosurgical Gas Analysis and Artificial Intelligence
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The aim of this clinical trial is to assess the feasibility of training a device capable of distinguishing various gases emitted by tissues cauterized by an electrosurgical unit during a breast cancer resection surgery. The patients to be enrolled will be women over 18 years old diagnosed with breast cancer who are indicated for conservative breast cancer resection surgery as treatment. The main questions to be answered are: The specificity and sensitivity of the device in detecting margins compromised with tumor cells in resection surgeries. Evaluate the applicability of the device in breast cancer surgeries for real-time detection of margins. Evaluate the differences in the pattern of gases emitted in tumor cells vs normal cells. By consenting, the study patients will allow the investigative team to access the clinical record, results of images, post-surgical biopsies, recording of the surgery while preserving the patient's anonymity, and the installation of the gas detection device. This device does not alter the flow of the surgery and does not add additional risk to it.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- label
- Patients who have undergone conservative breast cancer surgery
- description
- This group will undergo an analysis of gases emitted by cauterization. This intervention will not modify the duration of the surgery, nor will it alter the patient's treatment and prognosis. The gases will be analyzed through the detection device, which will not have direct contact with the patient and will not influence the surgical outcomes of the resection surgery. The patient will be followed for 2 years through their clinical care records in the UC Christus health network, looking for the occurrence of disease recurrence.
Primary outcomes (1)
- measure
- Differentiation of cancerous tissue from normal tissue
- timeFrame
- Evaluation will be conducted 2 months post-surgery, comparing the biopsy results with the classification made by the device.
- description
- Specificity, sensitivity, and accuracy of the device and software in tissue classification
Secondary outcomes (4)
- measure
- Applicability to the surgical workflow
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Histologically confirmed diagnosis of malignant breast cancer * Scheduled for BCS at the Hospital UC * Able and willing to provide informed consent Exclusion Criteria: * Pregnant or lactating women * Patients with known hypersensitivity or allergy to any component of the BCGC device * Participation in another interventional clinical trial within 30 days prior to enrolment
References
Publications (43)
- BACKGROUNDMcKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A, Etemadi M, Garcia-Vicente F, Gilbert FJ, Halling-Brown M, Hassabis D, Jansen S, Karthikesalingam A, Kelly CJ, King D, Ledsam JR, Melnick D, Mostofi H, Peng L, Reicher JJ, Romera-Paredes B, Sidebottom R, Suleyman M, Tse D, Young KC, De Fauw J, Shetty S. International evaluation of an AI system for breast cancer screening. Nature. 2020 Jan;577(7788):89-94. doi: 10.1038/s41586-019-1799-6. Epub 2020 Jan 1. PMID 31894144
- BACKGROUNDLeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539. PMID 26017442
- BACKGROUNDKeelan S, Flanagan M, Hill ADK. Evolving Trends in Surgical Management of Breast Cancer: An Analysis of 30 Years of Practice Changing Papers. Front Oncol. 2021 Aug 4;11:622621. doi: 10.3389/fonc.2021.622621. eCollection 2021. PMID 34422626
- BACKGROUNDSingla N, Dubey K, Srivastava V. Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network. J Biophotonics. 2019 Mar;12(3):e201800255. doi: 10.1002/jbio.201800255. Epub 2018 Nov 13. PMID 30318761
- BACKGROUNDEhteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak JAWM; the CAMELYON16 Consortium; Hermsen M, Manson QF, Balkenhol M, Geessink O, Stathonikos N, van Dijk MC, Bult P, Beca F, Beck AH, Wang D, Khosla A, Gargeya R, Irshad H, Zhong A, Dou Q, Li Q, Chen H, Lin HJ, Heng PA, Hass C, Bruni E, Wong Q, Halici U, Oner MU, Cetin-Atalay R, Berseth M, Khvatkov V, Vylegzhanin A, Kraus O, Shaban M, Rajpoot N, Awan R, Sirinukunwattana K, Qaiser T, Tsang YW, Tellez D, Annuscheit J, Hufnagl P, Valkonen M, Kartasalo K, Latonen L, Ruusuvuori P, Liimatainen K, Albarqouni S, Mungal B, George A, Demirci S, Navab N, Watanabe S, Seno S, Takenaka Y, Matsuda H, Ahmady Phoulady H, Kovalev V, Kalinovsky A, Liauchuk V, Bueno G, Fernandez-Carrobles MM, Serrano I, Deniz O, Racoceanu D, Venancio R. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017 Dec 12;318(22):2199-2210. doi: 10.1001/jama.2017.14585.