Breast CancerNCT07131735
Intraoperative Detection of Breast Cancer by Electrosurgical Gas Analysis and Artificial Intelligence
An observational study of Breast Cancer, run by Pontificia Universidad Catolica de Chile at 2 sites in Chile.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Breast Cancer
- How many people
- 30 participants
- Where
- 2 sitesChile
- Study dates
- 18 Dec 2023to 18 Sep 2025
- How long will I take part?
- 3 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: The study will recruit women from all over Chile who are indicated for conservative breast cancer surgery. It is estimated that the majority of patients will be those from Santiago de Chile who receive care from professionals in the UC Christus Health Network and decide to have surgery at the UC CHRISTUS Clinical Hospital.
Who may be able to join
- Histologically confirmed diagnosis of malignant breast cancer
- Scheduled for BCS at the Hospital UC
- Able and willing to provide informed consent
Who cannot join
- 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
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Chile2 sites, 1 recruiting
- Recruiting
Hospital Clínico UC CHRISTUS
Santiago, Santiago Centro, 8320000
- Active, not recruiting
Facultad de medicina UC
Santiago, Santiago Metropolitan, 8330191
About this study
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:
Read more
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.
Read the 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.
- Conditions
- Breast Cancer
- Keywords
- Breast Cancer, odors and cancer, breast surgery, intraoperative margin assistant, artificial intelligence
- Medical subject headings
- Breast Neoplasms, Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Patients who have undergone conservative breast cancer surgery | 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Differentiation of cancerous tissue from normal tissue
Specificity, sensitivity, and accuracy of the device and software in tissue classification
Measured: Evaluation will be conducted 2 months post-surgery, comparing the biopsy results with the classification made by the device.
Secondary outcomes (4)
Applicability to the surgical workflow
Installation time and the surgeon's subjective assessment will be measured using the "Device Applicability to Surgical Workflow" survey, which comprises three questions for a total score of 3-15 points. The survey will be administered once to each surgeon.
Measured: The survey will be done 30 minutes after the conclusion of surgery and is estimated to require two minutes to complete.
Biocompatibility
Biocompatibility of the materials used will be assessed by documenting adverse reactions during the surgical procedure and the immediate postoperative period.
Measured: 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.
The device is capable of real-time detection of organic compounds, with a latency not exceeding 1.5 seconds, and exhibits variable response slopes depending on the specific volatile organic compounds to which it is exposed.
Measured: 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
Late complications related to device use will be assessed through reported occurrences.
Measured: 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.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Aug 2025
- Study start18 Dec 2023
- Primary completion18 Sep 2025 (estimated)
- Study completion18 Sep 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (43)Papers the sponsor has linked to this study.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- More trials run by Pontificia Universidad Catolica de ChileThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 30 participants (estimated)
- Target duration
- 3 Years
Sponsor and investigators
- Lead sponsor
- Pontificia Universidad Catolica de Chile
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07131735
- Sponsor's protocol ID
- 230719001
- Status last verified
- Jul 2025
- First submitted
- 28 Jul 2025
- Last updated
- 20 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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 system for breast cancer screening. Nature. 2020 Jan;577(7788):89-94. doi: 10.1038/s41586-019-1799-6. Epub 2020 Jan 1.
PubMed 31894144LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
PubMed 26017442Keelan 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.
PubMed 34422626Singla 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.
PubMed 30318761Ehteshami 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.
PubMed 29234806Chagpar 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.
PubMed 26028131Boppart 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.
PubMed 14999139Assayag 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.
PubMed 24000981Tang R, Saksena M, Coopey SB, Fernandez L, Buckley JM, Lei L, Aftreth O, Koerner F, Michaelson J, Rafferty E, Brachtel E, Smith BL. Intraoperative micro-computed tomography (micro-CT): a novel method for determination of primary tumour dimensions in breast cancer specimens. Br J Radiol. 2016;89(1058):20150581. doi: 10.1259/bjr.20150581. Epub 2015 Nov 16.
PubMed 26568439Qiu SQ, Dorrius MD, de Jongh SJ, Jansen L, de Vries J, Schroder CP, Zhang GJ, de Vries EGE, van der Vegt B, van Dam GM. Micro-computed tomography (micro-CT) for intraoperative surgical margin assessment of breast cancer: A feasibility study in breast conserving surgery. Eur J Surg Oncol. 2018 Nov;44(11):1708-1713. doi: 10.1016/j.ejso.2018.06.022. Epub 2018 Jul 3.
PubMed 30005963McClatchy DM 3rd, Zuurbier RA, Wells WA, Paulsen KD, Pogue BW. Micro-computed tomography enables rapid surgical margin assessment during breast conserving surgery (BCS): correlation of whole BCS micro-CT readings to final histopathology. Breast Cancer Res Treat. 2018 Dec;172(3):587-595. doi: 10.1007/s10549-018-4951-3. Epub 2018 Sep 17.
PubMed 30225621Janssen NNY, van Seijen M, Loo CE, Vrancken Peeters MTFD, Hankel T, Sonke JJ, Nijkamp J. Feasibility of Micro-Computed Tomography Imaging for Direct Assessment of Surgical Resection Margins During Breast-Conserving Surgery. J Surg Res. 2019 Sep;241:160-169. doi: 10.1016/j.jss.2019.03.029. Epub 2019 Apr 23.
PubMed 31026794DiCorpo D, Tiwari A, Tang R, Griffin M, Aftreth O, Bautista P, Hughes K, Gershenfeld N, Michaelson J. The role of Micro-CT in imaging breast cancer specimens. Breast Cancer Res Treat. 2020 Apr;180(2):343-357. doi: 10.1007/s10549-020-05547-z. Epub 2020 Feb 4.
PubMed 32020431van Riet PA, Giorgio Arcidiacono P, Petrone M, Quoc Nguyen N, Kitano M, Chang K, Larghi A, Iglesias-Garcia J, Giovannini M, van der Merwe S, Santo E, Baldaque-Silva F, Bucobo JC, Bruno MJ, Aslanian HR, Cahen DL, Farrell J. Combined versus single use 20 G fine-needle biopsy and 25 G fine-needle aspiration for endoscopic ultrasound-guided tissue sampling of solid gastrointestinal lesions. Endoscopy. 2020 Jan;52(1):37-44. doi: 10.1055/a-0966-8755. Epub 2019 Jul 22.
PubMed 31330556Lange M, Reimer T, Hartmann S, Glass A, Stachs A. The role of specimen radiography in breast-conserving therapy of ductal carcinoma in situ. Breast. 2016 Apr;26:73-9. doi: 10.1016/j.breast.2015.12.014. Epub 2016 Feb 1.
PubMed 27017245
Study at a glance
From the registry record, last updated by the sponsor on 20 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Intraoperative Detection of Breast Cancer by Electrosurgical Gas Analysis and Artificial Intelligence
- ClinicalTrials.gov ID
- NCT07131735
- Acronym / sponsor ID
- 230719001
- Conditions
- Breast Cancer
- Keywords
- Breast Cancer, odors and cancer, breast surgery, intraoperative margin assistant, artificial intelligence
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 30 participants (estimated)
- Lead sponsor
- Pontificia Universidad Catolica de ChileUniversity, hospital or other
- Study start
- 18 Dec 2023 (actual)
- Primary completion
- 18 Sep 2025 (estimated)
- Study completion
- 18 Sep 2025 (estimated)
- First posted
- 20 Aug 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Target follow-up
- 3 Years
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients who have undergone conservative breast cancer surgery | – | –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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study will recruit women from all over Chile who are indicated for conservative breast cancer surgery. It is estimated that the majority of patients will be those from Santiago de Chile who receive care from professionals in the UC Christus Health Network and decide to have surgery at the UC CHRISTUS Clinical Hospital.
Key inclusion criteria (3 listed)
- Histologically confirmed diagnosis of malignant breast cancer
- Scheduled for BCS at the Hospital UC
- Able and willing to provide informed consent
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Specificity, sensitivity, and accuracy of the device and software in tissue classification
4 secondary outcomes
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Aug 2025.
- Background: PubMed 31894144McKinney 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.
- Background: PubMed 26017442LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
- Background: PubMed 34422626Keelan 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.
- Background: PubMed 30318761Singla 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.
- Background: PubMed 29234806Ehteshami 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.
- Background: PubMed 26028131Chagpar 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.
- Background: PubMed 14999139Boppart 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.
- Background: PubMed 24000981Assayag 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.
- Background: PubMed 26568439Tang R, Saksena M, Coopey SB, Fernandez L, Buckley JM, Lei L, Aftreth O, Koerner F, Michaelson J, Rafferty E, Brachtel E, Smith BL. Intraoperative micro-computed tomography (micro-CT): a novel method for determination of primary tumour dimensions in breast cancer specimens. Br J Radiol. 2016;89(1058):20150581. doi: 10.1259/bjr.20150581. Epub 2015 Nov 16.
- Background: PubMed 30005963Qiu SQ, Dorrius MD, de Jongh SJ, Jansen L, de Vries J, Schroder CP, Zhang GJ, de Vries EGE, van der Vegt B, van Dam GM. Micro-computed tomography (micro-CT) for intraoperative surgical margin assessment of breast cancer: A feasibility study in breast conserving surgery. Eur J Surg Oncol. 2018 Nov;44(11):1708-1713. doi: 10.1016/j.ejso.2018.06.022. Epub 2018 Jul 3.
- Background: PubMed 30225621McClatchy DM 3rd, Zuurbier RA, Wells WA, Paulsen KD, Pogue BW. Micro-computed tomography enables rapid surgical margin assessment during breast conserving surgery (BCS): correlation of whole BCS micro-CT readings to final histopathology. Breast Cancer Res Treat. 2018 Dec;172(3):587-595. doi: 10.1007/s10549-018-4951-3. Epub 2018 Sep 17.
- Background: PubMed 31026794Janssen NNY, van Seijen M, Loo CE, Vrancken Peeters MTFD, Hankel T, Sonke JJ, Nijkamp J. Feasibility of Micro-Computed Tomography Imaging for Direct Assessment of Surgical Resection Margins During Breast-Conserving Surgery. J Surg Res. 2019 Sep;241:160-169. doi: 10.1016/j.jss.2019.03.029. Epub 2019 Apr 23.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by Pontificia Universidad Catolica de Chile at 2 sites in Chile.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 20 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Chile. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Maximiliano L Mariné (+56952084996, maxmarine@uc.cl), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07131735, which also lists 43 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Aug 2025.