Validation of the Diagnostic Accuracy of the Electronic Nose in the Detection of Thyroid Cancer
NCT ID: NCT04883294
Last Updated: 2021-05-12
Study Results
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Basic Information
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UNKNOWN
1500 participants
OBSERVATIONAL
2021-05-01
2025-01-01
Brief Summary
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A promising development in cancer detection is based on volatile organic compounds (VOCs), gaseous degradation products of biochemical processes detectable in exhaled breath. During pathophysiological processes related to tumor growth, alterations in cell metabolism lead to a shift in the production of VOCs. The VOCs' patterns can be detected by the Aeonose™ through their reaction with the metal-oxide sensors in this device. A pilot study conducted at the Maastricht University Medical Center demonstrated that, by creating an artificial neural network (ANN) from the VOC patterns of numerous patients and their specific histopathological diagnosis, the Aeonose™ has a high diagnostic accuracy to discriminate benign from malignant thyroid nodules.
The purpose of this study is to validate the accuracy of the Aeonose™, to prevent unnecessary surgery and to investigate the use of the Aeonose™ as a surveillance tool in the postoperative follow-up of differentiated thyroid cancer.
We hypothesize that the high negative predictive value of the pilot study will be confirmed in the validation study and expect that implementation of the Aeonose™ in clinical practice will subsequently reduce the number of unnecessary surgeries below 10% for patients with Bethesda ≥ III nodules and may provide an important role in non-invasive detection of recurrent disease.
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Detailed Description
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Patients often present themselves with a thyroid nodule, which can be benign or malignant. Current diagnostic work-up includes ultrasound of the neck, resulting in the Thyroid Imaging Reporting And Data System (TI-RADS). This risk-stratification system for thyroid nodules is based on ultrasound features. When indicated, fine-needle aspiration cytology (FNAC) is performed, resulting in the Bethesda classification to report the risk of malignancy. The FNAC has been considered as gold standard diagnostic procedure in suspected thyroid nodules . Unfortunately, both negative- and positive predictive value of FNAC is poor, often resulting in the need for a diagnostic hemithyroidectomy for definite diagnosis. 40-94% of the suspected thyroid nodules appear to be benign after resection. After surgical removal, patients with differentiated thyroid cancer sometimes receive adjuvant treatment with radioactive-iodine. Thereafter, patients face a long-term follow-up since recurrence of the disease may occur years after removal of the primary tumor. Based on risk stratification, serum thyroglobulin (Tg) levels are determined once or twice per year with or without additional imaging (e.g. thyroid ultrasound or total body scintigraphy). So, both diagnosis and follow-up of thyroid cancer are associated with invasive and unpleasant procedures for the patient.
After surgical removal, patients with differentiated thyroid cancer sometimes receive adjuvant treatment with radioactive-iodine. Thereafter, patients will be subjected to a long-term follow- up since recurrence of the disease may occur years after removal of the primary tumor. Based on risk stratification, serum thyroglobulin (Tg) levels are determined once or twice per year with or without additional imaging (e.g. thyroid ultrasound or total body scintigraphy). So, both diagnosis and follow-up of thyroid cancer are associated with invasive and unpleasant procedures for the patient.
A promising development in cancer detection is based on volatile organic compounds (VOCs), gaseous degradation products of biochemical processes detectable in exhaled breath. During pathophysiological processes related to tumor growth, alterations in cell metabolism lead to a shift in the production of VOCs. The VOCs' patterns can be detected by the Aeonose™ through their reaction to the metal-oxide sensors in this device. A pilot study conducted at the Maastricht University Medical Center+ demonstrated that, by creating an artificial neural network (ANN) from the VOC patterns of numerous patients and their specific histopathological diagnosis, the Aeonose™ has a high diagnostic accuracy to discriminate benign from malignant thyroid nodules.
The purpose of this study is to validate the accuracy of the Aeonose™, to prevent unnecessary surgery and to investigate the use of the Aeonose™ as a surveillance tool in the postoperative follow-up of differentiated thyroid cancer.
Aim
This study consists of three aims:
1. Confirm the results of the pilot study on the use of the Aeonose™ to distinguish benign from malignant thyroid nodules in a multicenter validation study.
2. Implement the use of the Aeonose™ in clinical practice, aiming to reduce unnecessary surgeries to \<10%.
3. Investigate the use of the Aeonose™ to detect recurrent thyroid cancer in a patient friendly, non-invasive way compared to regular follow-up in a pilot study.
We plan to conduct an international prospective, observational multicenter study in twelve hospitals in the Netherlands, Belgium and Germany to validate the diagnostic performance of the Aeonose™ and to investigate its efficacy in the follow-up of differentiated thyroid cancer. First, a database of breathing patterns will be built to develop an ANN. An external validation study will be conducted using exhaled breath patterns of a total of 500 patients to address the first aim. Data from the validation study together with clinical parameters will be used to determine the necessity for a diagnostic hemithyroidectomy. The number of unnecessary surgeries will be evaluated. To address the third aim, a pilot study will be conducted. Patients with histologically proven differentiated thyroid cancer are asked to breathe in the Aeonose™ parallel to every regular follow-up moment. The Aeonose™ will be trained by the input of data of patients developing a recurrence or not to develop an ANN that can be used as a surveillance tool to detect recurrence non-invasively.
Before starting the validation study, the artificial neural network (ANN) that has been built in the pilot study will be expanded in the training study. The more patients included with a variety in breathing patterns (as a result of e.g. gender, age, smoking-status, food intake, medication use), the more stable and robust the model becomes. For this training study, a total of 200 patients with histopathological proven differentiated thyroid cancer is needed. Assuming that the prevalence of thyroid cancer among suspected thyroid nodules is 20%, a total of 1000 patients would be required for the training study. Data of patients who participated in the pilot study will be used in the training study. The training study will be carried out in all 12 hospitals.
Validation study Directly after finishing the training study, the validation study will be started in 'new' patients that have not participated in the training study.
Every patient with a suspected thyroid nodule, indicated for additional diagnostic procedures including an ultrasound of the thyroid gland (resulting in the TIRADS classification) followed by a fine-needle aspiration for cytology (resulting in the Bethesda classification), will be asked to participate. We will provide information about the study and obtain a signed informed consent. We expect that in particular, the FNAC might influence the patterns of the VOCs in the exhaled breath, measured by the Aeonose™. For this reason, we will include patients in this study before FNAC will take place.
Patients in the validation study will be asked to participate only once by breathing in the Aeonose™ for five minutes, followed by completing a short questionnaire of known factors that can influence the breathing patterns.
A minimum of 500 patients will be necessary to validate the sensitivity of the pilot study.
This study has no specific benefits for the participating patients. The risks of using the Aeonose are very small. Possible side effects during measurements are dizziness and nausea, usually due to hyperventilation. Other side effects are hypo- or hyper salivation during measurements. Blood will be collected during regular pre-operative blood collection, resulting in no extra skin puncture. Furthermore two quick to answer questionnaires as well as a short case report form (CRF) will be filled in by the participating patients. Using the Aeonose as a non-invasive, rapid and inexpensive diagnostic tool could be a major benefit for patients with thyroid nodules due to the faster and less invasive diagnostic process. Benefits for patients with benign thyroid diseases include the possibility to resign from unnecessary invasive treatments such as (diagnostic) surgery.
We hypothesize that the high negative predictive value of the pilot study will be confirmed in the validation study and expect that implementation of the Aeonose™ in clinical practice will subsequently reduce the number of unnecessary surgeries below 10% for patients with Bethesda ≥ III nodules. For the pilot study on the follow-up, we hypothesize that the Aeonose™ can play an important role in non-invasive detection of recurrent disease. Within several years, analysis of exhaled breath using the Aeonose™ will play an important role in the detection of thyroid cancer and/or its recurrence and will, therefore, have a position in regular clinical decision making
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Disease Group
A disease group including patients older than 18 years old with any kind of histopathologically proven malignancy of the thyroid gland (including Papillary thyroid cancer (PTC), Follicular thyroid cancer (FTC), Medullary thyroid cancer (MTC), and Anaplastic thyroid cancer);
Electronic Nose
All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine-learning and used for pattern recognition. A nose clip was placed on the nose of each participant to avoid entry of non-filtered air in the device. Before measuring, the Aeonose was flushed with room air, guided through a carbon filter as well. Failed breath tests were excluded from analysis; the reason for failure was documented.
Control
A control group including patients older than 18 years old with any kind of histopathologically proven benign thyroid gland disease (including adenoma, hyperplasia).
Electronic Nose
All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine-learning and used for pattern recognition. A nose clip was placed on the nose of each participant to avoid entry of non-filtered air in the device. Before measuring, the Aeonose was flushed with room air, guided through a carbon filter as well. Failed breath tests were excluded from analysis; the reason for failure was documented.
Interventions
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Electronic Nose
All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine-learning and used for pattern recognition. A nose clip was placed on the nose of each participant to avoid entry of non-filtered air in the device. Before measuring, the Aeonose was flushed with room air, guided through a carbon filter as well. Failed breath tests were excluded from analysis; the reason for failure was documented.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
* Patients with thyroid problems requiring surgery (e.g. goiter)
* AeonoseTM measurement before undergoing cytological puncture or at least 3 days after cytological puncture pre-operatively.
* \> 18 year.
* Signed informed consent
Exclusion Criteria
* Not able to understand the information
18 Years
ALL
No
Sponsors
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Maastricht University Medical Center
OTHER
Responsible Party
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Principal Investigators
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Nicole Bouvy, Prof,MD,PhD
Role: PRINCIPAL_INVESTIGATOR
Maastricht University Medical Center
Central Contacts
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References
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Pellegriti G, Frasca F, Regalbuto C, Squatrito S, Vigneri R. Worldwide increasing incidence of thyroid cancer: update on epidemiology and risk factors. J Cancer Epidemiol. 2013;2013:965212. doi: 10.1155/2013/965212. Epub 2013 May 7.
Tessler FN, Middleton WD, Grant EG. Thyroid Imaging Reporting and Data System (TI-RADS): A User's Guide. Radiology. 2018 Apr;287(1):29-36. doi: 10.1148/radiol.2017171240.
Tamhane S, Gharib H. Thyroid nodule update on diagnosis and management. Clin Diabetes Endocrinol. 2016 Oct 3;2:17. doi: 10.1186/s40842-016-0035-7. eCollection 2016.
Cibas ES, Ali SZ; NCI Thyroid FNA State of the Science Conference. The Bethesda System For Reporting Thyroid Cytopathology. Am J Clin Pathol. 2009 Nov;132(5):658-65. doi: 10.1309/AJCPPHLWMI3JV4LA.
Haick H, Broza YY, Mochalski P, Ruzsanyi V, Amann A. Assessment, origin, and implementation of breath volatile cancer markers. Chem Soc Rev. 2014 Mar 7;43(5):1423-49. doi: 10.1039/c3cs60329f. Epub 2013 Dec 4.
de Lacy Costello B, Amann A, Al-Kateb H, Flynn C, Filipiak W, Khalid T, Osborne D, Ratcliffe NM. A review of the volatiles from the healthy human body. J Breath Res. 2014 Mar;8(1):014001. doi: 10.1088/1752-7155/8/1/014001. Epub 2014 Jan 13.
Guo L, Wang C, Chi C, Wang X, Liu S, Zhao W, Ke C, Xu G, Li E. Exhaled breath volatile biomarker analysis for thyroid cancer. Transl Res. 2015 Aug;166(2):188-95. doi: 10.1016/j.trsl.2015.01.005. Epub 2015 Jan 20.
Hoftijzer HC, Heemstra KA, Corssmit EP, van der Klaauw AA, Romijn JA, Smit JW. Quality of life in cured patients with differentiated thyroid carcinoma. J Clin Endocrinol Metab. 2008 Jan;93(1):200-3. doi: 10.1210/jc.2007-1203. Epub 2007 Oct 23.
Barbus E, Pestean C, Larg MI, Piciu D. Quality of life in thyroid cancer patients: a literature review. Clujul Med. 2017;90(2):147-153. doi: 10.15386/cjmed-703. Epub 2017 Apr 25.
Al-Difaie ZJJ, Scheepers MHMC, Engelen SME, Lubbers T, Havekes B, Bouvy ND. Volatile organic compounds in exhaled breath, blood, and urine detected in patients with thyroid carcinoma using gas chromatography-ion mobility spectrometry-a pilot study. J Breath Res. 2024 Nov 26;19(1). doi: 10.1088/1752-7163/ad89ef.
Other Identifiers
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NL76036.068.21
Identifier Type: -
Identifier Source: org_study_id
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