CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology
Lung cancer remains the leading cause of cancer related mortality worldwide, with more than 1.5 million related deaths annually. Lung cancer is divided into two main groups: Small Cell Lung Carcinoma (SCLC) and Non-Small Cell Lung Carcinoma (NSCLC), with prevalence of ~20% and 80% respectively. NSCLC is further subdivided into adenocarcinoma (the most common), squamous cell carcinoma (SCC), and large cell carcinoma. Furthermore, each subtype is likely to have specific mutations, which could be targeted for treatment.
Medical imaging and radiomics feature extraction represent a candidate alternative to conventional tissue biopsy, a theory that is investigated in this study.
연구 개요
연구 유형
등록 (예상)
연락처 및 위치
연구 장소
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Limburg
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Maastricht, Limburg, 네덜란드, 6229ER
- Maastricht University
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참여기준
자격 기준
공부할 수 있는 나이
- 어린이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
연구 대상 성별
샘플링 방법
연구 인구
설명
Inclusion Criteria:
- Availability of diagnostic non-contrast enhanced CT scan.
- Availability of histologic tumor analysis results
Exclusion Criteria:
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공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
코호트 및 개입
그룹/코호트 |
개입 / 치료 |
|---|---|
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Maastro (Lung1)
Open source dataset available at TCIA.org.
The cohort includes CT scans of 422 patients diagnosed with NSCLC.
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Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
다른 이름들:
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UCSF
A cohort of patients diagnosed with NSCLC at UCSF medical center.
It includes CT scans of 165 patients.
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Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
다른 이름들:
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Radboud
A cohort of patients diagnosed with NSCLC at Radboud medical center.
It includes CT scans of 255 patients.
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Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
다른 이름들:
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Stanford
Open source dataset available at TCIA.org.
The cohort includes CT scans of 211 patients diagnosed with NSCLC.
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Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
다른 이름들:
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Lung histology
기간: December 2019
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Is the tumor under investigation an adenocarcinoma of the lung?
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December 2019
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공동 작업자 및 조사자
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (예상)
연구 완료 (예상)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .