Ignite Creation Date:
2025-12-24 @ 6:36 PM
Ignite Modification Date:
2025-12-24 @ 6:36 PM
Study NCT ID:
NCT04271657
Status:
COMPLETED
Last Update Posted:
2021-05-06
First Post:
2020-02-13
Is Possible Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
RadioPathomics Artificial Intelligence Model to Predict nCRT Response in Locally Advanced Rectal Cancer
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D012004', 'term': 'Rectal Neoplasms'}, {'id': 'D000095384', 'term': 'Pathologic Complete Response'}], 'ancestors': [{'id': 'D015179', 'term': 'Colorectal Neoplasms'}, {'id': 'D007414', 'term': 'Intestinal Neoplasms'}, {'id': 'D005770', 'term': 'Gastrointestinal Neoplasms'}, {'id': 'D004067', 'term': 'Digestive System Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D005767', 'term': 'Gastrointestinal Diseases'}, {'id': 'D007410', 'term': 'Intestinal Diseases'}, {'id': 'D012002', 'term': 'Rectal Diseases'}, {'id': 'D018450', 'term': 'Disease Progression'}, {'id': 'D020969', 'term': 'Disease Attributes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 100}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2020-01-10', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-05', 'completionDateStruct': {'date': '2020-12-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2021-05-01', 'studyFirstSubmitDate': '2020-02-13', 'studyFirstSubmitQcDate': '2020-02-13', 'lastUpdatePostDateStruct': {'date': '2021-05-06', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2020-02-17', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2020-11-09', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathomics artificial intelligence model', 'timeFrame': 'baseline', 'description': 'The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathomics artificial intelligence model for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.'}], 'secondaryOutcomes': [{'measure': 'The specificity of the radiopathomics artificial intelligence model', 'timeFrame': 'baseline', 'description': 'The specificity of the radiopathomics artificial intelligence model for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.'}, {'measure': 'The sensitivity of the radiopathomics artificial intelligence model', 'timeFrame': 'baseline', 'description': 'The sensitivity of the radiopathomics artificial intelligence model for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Radiopathomics features', 'Artificial intelligence', 'Locally advanced rectal cancer', 'Pathologic complete response', 'Neoadjuvant chemoradiotherapy'], 'conditions': ['Rectal Cancer']}, 'referencesModule': {'references': [{'pmid': '34952679', 'type': 'DERIVED', 'citation': 'Feng L, Liu Z, Li C, Li Z, Lou X, Shao L, Wang Y, Huang Y, Chen H, Pang X, Liu S, He F, Zheng J, Meng X, Xie P, Yang G, Ding Y, Wei M, Yun J, Hung MC, Zhou W, Wahl DR, Lan P, Tian J, Wan X. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health. 2022 Jan;4(1):e8-e17. doi: 10.1016/S2589-7500(21)00215-6.'}]}, 'descriptionModule': {'briefSummary': 'In this study, investigators utilize a radiopathomics integrated Artificial Intelligence (AI) supportive system to predict tumor response to neoadjuvant chemoradiotherapy (nCRT) before its administration for patients with locally advanced rectal cancer (LARC). By the system, whether the participants achieve the pathologic complete response (pCR) will be identified based on the radiopathomics features extracted from the pre-nCRT Magnetic Resonance Imaging (MRI) and biopsy images. The predictive power to discriminate the pCR individuals from non-pCR patients, will be validated in this multicenter, prospective clinical study.', 'detailedDescription': 'This is a multicenter, prospective, observational clinical study for validation of a radiopathomics artificial intelligence (AI) system. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III staging without distant metastasis by enhanced Magnetic Resonance Imaging (MRI) will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, the Third Affiliated Hospital of Kunming Medical College and Sir Run Run Shaw Hospital Affiliated by Zhejiang University School of Medicine. All participants should follow a very standard treatment protocol, including of concurrent neoadjuvant chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and adjuvant chemotherapy. The MRI and biopsy examination should be completed before the nCRT and the images will be subjected to the manual delineation of the tumor regions of interest (ROI) by experienced radiologists and pathologists. Subsequently, the outlined MRI and biopsy slides images will be employed to the radiopathomics AI system to generate the predicted response ("predicted pathologic complete response (pCR)" vs. "predicted non-pCR") of individual patient, whereas the actual response ("pathologic confirmed as pCR" vs. "pathologic confirmed as non-pCR") will be diagnosed at surgery excised specimen. Through comparisons of the predicted responses and true pathologic responses, investigators calculate the prediction accuracy, specificity, sensitivity as well as the Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curves. This study is aimed to validate the high accuracy and robustness of the radiopathomics AI system for identifying pCR candidates from non-pCR individuals before nCRT which will facilitate further precision therapy for patients with locally advanced rectal cancer.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The population in the study are the patients with LARC, who are intended to receive or undergoing standard, neoadjuvant concurrent chemoradiotherapy with tumor pathologic response unknown.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* pathologically diagnosed as rectal adenocarcinoma\n* defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis by enhanced Magnetic Resonance Imaging (MRI)\n* intending to receive or undergoing neoadjuvant concurrent chemoradiotherapy (5-fluorouracil based chemotherapy, given orally or intravenously; Intensity-Modulated Radiotherapy or Volume-Modulated Radiotherapy delivered at 50 gray (Gy) in gross tumor volume (GTV) and 45 Gy in clinical target volume (CTV) by 25 fractions)\n* intending to receive total mesorectum excision (TME) surgery after neoadjuvant therapy (not completed at the enrollment), and adjuvant chemotherapy\n* MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed before the neoadjuvant chemoradiotherapy\n* biopsy H\\&E stained slides are available and scanned with high resolution before the neoadjuvant chemoradiotherapy\n\nExclusion Criteria:\n\n* with history of other cancer\n* insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts)\n* insufficient imaging quality of biopsy slides imaging to delineate tumor volume or obtain measurements (e.g., tissue dissection, color anomaly)\n* incomplete neoadjuvant chemoradiotherapy\n* no surgery after neoadjuvant chemoradiotherapy resulting in lack of pathologic assessment of tumor response\n* tumor recurrence or distant metastasis during neoadjuvant chemoradiotherapy'}, 'identificationModule': {'nctId': 'NCT04271657', 'acronym': 'RPAI-pCR', 'briefTitle': 'RadioPathomics Artificial Intelligence Model to Predict nCRT Response in Locally Advanced Rectal Cancer', 'organization': {'class': 'OTHER', 'fullName': 'Sixth Affiliated Hospital, Sun Yat-sen University'}, 'officialTitle': 'A RadioPathomics Integrated Artificial Intelligence System to Predict Neoadjuvant Chemoradiotherapy Response for Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study', 'orgStudyIdInfo': {'id': 'RPAI-pCR2020'}}, 'contactsLocationsModule': {'locations': [{'zip': '510655', 'city': 'Guangzhou', 'state': 'Guangdong', 'country': 'China', 'facility': 'the Sixth Affiliated Hospital of Sun Yat-sen University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}, {'zip': '650000', 'city': 'Kunming', 'state': 'Yunnan', 'country': 'China', 'facility': 'The Third Affiliated Hospital of Kunming Medical College', 'geoPoint': {'lat': 25.03889, 'lon': 102.71833}}, {'zip': '310000', 'city': 'Hangzhou', 'state': 'Zhejiang', 'country': 'China', 'facility': 'Sir Run Run Shaw Hospital', 'geoPoint': {'lat': 30.29365, 'lon': 120.16142}}], 'overallOfficials': [{'name': 'Xinjuan Fan, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Sixth Affiliated Hospital, Sun Yat-sen University'}, {'name': 'Xiangbo Wan, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Sixth Affiliated Hospital, Sun Yat-sen University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sixth Affiliated Hospital, Sun Yat-sen University', 'class': 'OTHER'}, 'collaborators': [{'name': 'The Third Affiliated Hospital of Kunming Medical College.', 'class': 'OTHER'}, {'name': 'Sir Run Run Shaw Hospital', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor of Radiation Oncology, Vice Director, Department of Radiation Oncology', 'investigatorFullName': 'wanxiangbo', 'investigatorAffiliation': 'Sixth Affiliated Hospital, Sun Yat-sen University'}}}}