Intraoperative Detection of Breast Cancer by Electrosurgical Gas Analysis and Artificial Intelligence

Overview

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.

Detailed description

The study will consist of measuring gases emitted during surgery. Prior to this, the device will be trained in the detection and recognition of the most prevalent gases in cancerous tissue. This will enable the AI to detect and classify cancerous breast tissue from healthy tissue. The study will include patients with different types and subtypes of breast cancer, such as ductal carcinoma in situ invasive ductal carcinoma, invasive lobular carcinoma, triple-negative breast cancer, human epidermal growth factor receptor 2 positive breast cancer, and hormone receptor-positive breast cancer. These types and subtypes reflect the diversity and complexity of breast cancer and may influence the performance and accuracy of the BCGC device.

During the clinical testing phase, the laboratory-acquired tissue detection capability will be evaluated in a real situation. For this, the device will be connected to a sterile PVC hose directly connected to a smoke extractor associated with the electrosurgical unit that will cauterize the tissues. To evaluate the sensitivity, specificity, and accuracy of the device, the AI's detection will be compared with the quick biopsy and the deferred biopsy of the surgical piece. For this, access will be obtained to the histological reports and the surgery will be recorded to identify margins.

The device will be calibrated once a month with gases of known nature, a maximum variability of 5% will be tolerated. If a greater variability is detected at the time of calibration, it will be changed to every 2 weeks. The information will be anonymized and stored on an SSD unit and will be deleted 5 years after the completion of the study.

Primary outcome measures

• Differentiation of cancerous tissue from normal tissue [Time frame: Evaluation will be conducted 2 months post-surgery, comparing the biopsy results with the classification made by the device.]

Secondary outcome measures (4)

• Applicability to the surgical workflow [Time frame: The survey will be done 30 minutes after the conclusion of surgery and is estimated to require two minutes to complete.]
• Biocompatibility [Time frame: Adverse reactions will be evaluated from the moment of intervention up to 48 hours after the surgical procedure]
• Detecting volatile organic compounds in breast cancer tissue. [Time frame: Measurement will occur intraoperatively from the surgeon's first electrosurgical incision until removal of the primary specimen-typically within the first hour of surgery. This timeframe applies to every analyzed procedure.]
• Complications associated with the use of the device [Time frame: Complications associated with device use will be assessed during the late postoperative period, defined as 48 hours after surgery up to one month post-intervention.]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Other

Study locations

Chile · 2 centers

• Hospital Clínico UC CHRISTUS — Santiago
• Facultad de medicina UC — Santiago

Publications

• McKinney 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 sys PMID 31894144
• LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539. PMID 26017442
• Keelan 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
• Singla 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
• Ehteshami 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, Kh PMID 29234806
• Chagpar AB, Killelea BK, Tsangaris TN, Butler M, Stavris K, Li F, Yao X, Bossuyt V, Harigopal M, Lannin DR, Pusztai L, Horowitz NR. A Randomized, Controlled Trial of Cavity Shave Margins in Breast Cancer. N Engl J Med. 2015 Aug 6;373(6):503-10. doi: 10.1056/NEJMoa1504473. Epub 2015 May 30. PMID 26028131
• Boppart SA, Luo W, Marks DL, Singletary KW. Optical coherence tomography: feasibility for basic research and image-guided surgery of breast cancer. Breast Cancer Res Treat. 2004 Mar;84(2):85-97. doi: 10.1023/B:BREA.0000018401.13609.54. PMID 14999139
• Assayag O, Antoine M, Sigal-Zafrani B, Riben M, Harms F, Burcheri A, Grieve K, Dalimier E, Le Conte de Poly B, Boccara C. Large field, high resolution full-field optical coherence tomography: a pre-clinical study of human breast tissue and cancer assessment. Technol Cancer Res Treat. 2014 Oct;13(5):455-68. doi: 10.7785/tcrtexpress.2013.600254. Epub 2013 Aug 31. PMID 24000981

Identifiers

NCT: NCT07131735 · 230719001

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗