Computer Aided Nodule Analysis and Risk Yield (CANARY) characterization of adenocarcinoma: Radiologic biopsy, risk stratification and future directions

Ryan Clay, Srinivasan Rajagopalan, Ronald Karwoski, Fabien Maldonado, Tobias D Peikert, Brian Jack Bartholmai

Research output: Contribution to journalReview article

1 Citation (Scopus)

Abstract

The majority of incidentally and screen-detected lung cancers are adenocarcinomas. Optimal management of these tumors is clinically challenging due to variability in tumor histopathology and behavior. Invasive adenocarcinoma (IA) is generally aggressive while adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) may be extremely indolent. Computer Aided Nodule Analysis and Risk Yield (CANARY) is a quantitative computed tomography (CT) analysis tool that allows non-invasive assessment of tumor characteristics. This analysis may obviate the need for tissue biopsy and facilitate the risk stratification of adenocarcinoma of the lung. CANARY was developed by unsupervised machine learning techniques using CT data of histopathologically-characterized adenocarcinomas of the lung. This technique identified 9 distinct exemplars that constitute the spectrum of CT features found in adenocarcinoma of the lung. The distributions of these features in a nodule correlate with histopathology. Further automated clustering of CANARY nodules defined three distinct groups that have distinctly different post-resection disease free survival (DFS). CANARY has been validated within the NLST cohort and multiple other cohorts. Using semi-automated segmentation as input to CANARY, there is excellent repeatability and interoperator correlation of results. Confirmation and longitudinal tracking of indolent adenocarcinoma with CANARY may ultimately add decision support in nuanced cases where surgery may not be in the best interest of the patient due to competing comorbidity. Currently under investigation is CANARY's role in detecting differing driver mutations and tumor response to targeted chemotherapeutics. Combining the results from CANARY analysis with clinical information and other quantitative techniques such as analysis of the tumorfree surrounding lung may aid in building more powerful predictive models. The next step in CANARY investigation will be its prospective application, both in selecting low-risk stage 1 adenocarcinoma for active surveillance and investigation in selecting high-risk early stage adenocarcinoma for adjuvant therapy.

Original languageEnglish (US)
Pages (from-to)313-326
Number of pages14
JournalTranslational Lung Cancer Research
Volume7
Issue number3
DOIs
StatePublished - Jun 1 2018

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Adenocarcinoma
Biopsy
Tomography
Neoplasms
Direction compound
Disease-Free Survival
Cluster Analysis
Comorbidity
Lung Neoplasms
Lung
Mutation
Adenocarcinoma of lung

Keywords

  • Adenocarcinoma of the lung
  • Computer Aided Nodule Analysis and Risk Yield (CANARY)
  • Non-small cell lung cancer (NSCLC)
  • Quantitative CT analysis

ASJC Scopus subject areas

  • Oncology

Cite this

Computer Aided Nodule Analysis and Risk Yield (CANARY) characterization of adenocarcinoma : Radiologic biopsy, risk stratification and future directions. / Clay, Ryan; Rajagopalan, Srinivasan; Karwoski, Ronald; Maldonado, Fabien; Peikert, Tobias D; Bartholmai, Brian Jack.

In: Translational Lung Cancer Research, Vol. 7, No. 3, 01.06.2018, p. 313-326.

Research output: Contribution to journalReview article

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abstract = "The majority of incidentally and screen-detected lung cancers are adenocarcinomas. Optimal management of these tumors is clinically challenging due to variability in tumor histopathology and behavior. Invasive adenocarcinoma (IA) is generally aggressive while adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) may be extremely indolent. Computer Aided Nodule Analysis and Risk Yield (CANARY) is a quantitative computed tomography (CT) analysis tool that allows non-invasive assessment of tumor characteristics. This analysis may obviate the need for tissue biopsy and facilitate the risk stratification of adenocarcinoma of the lung. CANARY was developed by unsupervised machine learning techniques using CT data of histopathologically-characterized adenocarcinomas of the lung. This technique identified 9 distinct exemplars that constitute the spectrum of CT features found in adenocarcinoma of the lung. The distributions of these features in a nodule correlate with histopathology. Further automated clustering of CANARY nodules defined three distinct groups that have distinctly different post-resection disease free survival (DFS). CANARY has been validated within the NLST cohort and multiple other cohorts. Using semi-automated segmentation as input to CANARY, there is excellent repeatability and interoperator correlation of results. Confirmation and longitudinal tracking of indolent adenocarcinoma with CANARY may ultimately add decision support in nuanced cases where surgery may not be in the best interest of the patient due to competing comorbidity. Currently under investigation is CANARY's role in detecting differing driver mutations and tumor response to targeted chemotherapeutics. Combining the results from CANARY analysis with clinical information and other quantitative techniques such as analysis of the tumorfree surrounding lung may aid in building more powerful predictive models. The next step in CANARY investigation will be its prospective application, both in selecting low-risk stage 1 adenocarcinoma for active surveillance and investigation in selecting high-risk early stage adenocarcinoma for adjuvant therapy.",
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