Lung adenocarcinoma

Correlation of quantitative ct findings with pathologic findings

Jane P. Ko, James Suh, Opeyemi Ibidapo, Joanna G. Escalon, Jinyu Li, Harvey Pass, David P. Naidich, Bernard Crawford, Emily B. Tsai, Chi Wan Koo, Artem Mikheev, Henry Rusinek

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

Purpose: To identify the ability of computer-derived three-dimensional (3D) computed tomographic (CT) segmentation techniques to help differentiate lung adenocarcinoma subtypes. Materials and Methods: This study had institutional research board approval and was HIPAA compliant. Pathologically classified resected lung adenocarcinomas (n = 41) with thin-section CT data were identified. Two readers independently placed overinclusive volumes around nodules from which automated computer measurements were generated: mass (total mass) and volume (total volume) of the nodule and of any solid portion, in addition to the solid percentage of the nodule volume (percentage solid volume) or mass (percentage solid mass). Interobserver agreement and differences in measurements among pathologic entities were evaluated by using t tests. A multinomial logistic regression model was used to differentiate the probability of three diagnoses: invasive non-lepidic-predominant adenocarcinoma (INV), lepidic-predominant adenocarcinoma (LPA), and adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). Results: Mean percentage solid volume of INV was 35.4% (95% confidence interval [CI]: 26.2%, 44.5%)-higher than the 14.5% (95% CI: 10.3%, 18.7%) for LPA (P = .002). Mean percentage solid volume of AIS/MIA was 8.2% (95% CI: 2.7%, 13.7%) and had a trend toward being lower than that for LPA (P = .051). Accuracy of the model based on total volume and percentage solid volume was 73.2%; accuracy of the model based on total mass and percentage solid mass was 75.6%. Conclusion: Computer-assisted 3D measurement of nodules at CT had good reproducibility and helped differentiate among subtypes of lung adenocarcinoma.

Original languageEnglish (US)
Pages (from-to)931-939
Number of pages9
JournalRadiology
Volume280
Issue number3
DOIs
StatePublished - Sep 1 2016

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Adenocarcinoma
Confidence Intervals
Logistic Models
Health Insurance Portability and Accountability Act
Adenocarcinoma of lung
Research
Adenocarcinoma in Situ

ASJC Scopus subject areas

  • Medicine(all)
  • Radiology Nuclear Medicine and imaging

Cite this

Ko, J. P., Suh, J., Ibidapo, O., Escalon, J. G., Li, J., Pass, H., ... Rusinek, H. (2016). Lung adenocarcinoma: Correlation of quantitative ct findings with pathologic findings. Radiology, 280(3), 931-939. https://doi.org/10.1148/radiol.2016142975

Lung adenocarcinoma : Correlation of quantitative ct findings with pathologic findings. / Ko, Jane P.; Suh, James; Ibidapo, Opeyemi; Escalon, Joanna G.; Li, Jinyu; Pass, Harvey; Naidich, David P.; Crawford, Bernard; Tsai, Emily B.; Koo, Chi Wan; Mikheev, Artem; Rusinek, Henry.

In: Radiology, Vol. 280, No. 3, 01.09.2016, p. 931-939.

Research output: Contribution to journalArticle

Ko, JP, Suh, J, Ibidapo, O, Escalon, JG, Li, J, Pass, H, Naidich, DP, Crawford, B, Tsai, EB, Koo, CW, Mikheev, A & Rusinek, H 2016, 'Lung adenocarcinoma: Correlation of quantitative ct findings with pathologic findings', Radiology, vol. 280, no. 3, pp. 931-939. https://doi.org/10.1148/radiol.2016142975
Ko, Jane P. ; Suh, James ; Ibidapo, Opeyemi ; Escalon, Joanna G. ; Li, Jinyu ; Pass, Harvey ; Naidich, David P. ; Crawford, Bernard ; Tsai, Emily B. ; Koo, Chi Wan ; Mikheev, Artem ; Rusinek, Henry. / Lung adenocarcinoma : Correlation of quantitative ct findings with pathologic findings. In: Radiology. 2016 ; Vol. 280, No. 3. pp. 931-939.
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abstract = "Purpose: To identify the ability of computer-derived three-dimensional (3D) computed tomographic (CT) segmentation techniques to help differentiate lung adenocarcinoma subtypes. Materials and Methods: This study had institutional research board approval and was HIPAA compliant. Pathologically classified resected lung adenocarcinomas (n = 41) with thin-section CT data were identified. Two readers independently placed overinclusive volumes around nodules from which automated computer measurements were generated: mass (total mass) and volume (total volume) of the nodule and of any solid portion, in addition to the solid percentage of the nodule volume (percentage solid volume) or mass (percentage solid mass). Interobserver agreement and differences in measurements among pathologic entities were evaluated by using t tests. A multinomial logistic regression model was used to differentiate the probability of three diagnoses: invasive non-lepidic-predominant adenocarcinoma (INV), lepidic-predominant adenocarcinoma (LPA), and adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). Results: Mean percentage solid volume of INV was 35.4{\%} (95{\%} confidence interval [CI]: 26.2{\%}, 44.5{\%})-higher than the 14.5{\%} (95{\%} CI: 10.3{\%}, 18.7{\%}) for LPA (P = .002). Mean percentage solid volume of AIS/MIA was 8.2{\%} (95{\%} CI: 2.7{\%}, 13.7{\%}) and had a trend toward being lower than that for LPA (P = .051). Accuracy of the model based on total volume and percentage solid volume was 73.2{\%}; accuracy of the model based on total mass and percentage solid mass was 75.6{\%}. Conclusion: Computer-assisted 3D measurement of nodules at CT had good reproducibility and helped differentiate among subtypes of lung adenocarcinoma.",
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AU - Suh, James

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AU - Li, Jinyu

AU - Pass, Harvey

AU - Naidich, David P.

AU - Crawford, Bernard

AU - Tsai, Emily B.

AU - Koo, Chi Wan

AU - Mikheev, Artem

AU - Rusinek, Henry

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N2 - Purpose: To identify the ability of computer-derived three-dimensional (3D) computed tomographic (CT) segmentation techniques to help differentiate lung adenocarcinoma subtypes. Materials and Methods: This study had institutional research board approval and was HIPAA compliant. Pathologically classified resected lung adenocarcinomas (n = 41) with thin-section CT data were identified. Two readers independently placed overinclusive volumes around nodules from which automated computer measurements were generated: mass (total mass) and volume (total volume) of the nodule and of any solid portion, in addition to the solid percentage of the nodule volume (percentage solid volume) or mass (percentage solid mass). Interobserver agreement and differences in measurements among pathologic entities were evaluated by using t tests. A multinomial logistic regression model was used to differentiate the probability of three diagnoses: invasive non-lepidic-predominant adenocarcinoma (INV), lepidic-predominant adenocarcinoma (LPA), and adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). Results: Mean percentage solid volume of INV was 35.4% (95% confidence interval [CI]: 26.2%, 44.5%)-higher than the 14.5% (95% CI: 10.3%, 18.7%) for LPA (P = .002). Mean percentage solid volume of AIS/MIA was 8.2% (95% CI: 2.7%, 13.7%) and had a trend toward being lower than that for LPA (P = .051). Accuracy of the model based on total volume and percentage solid volume was 73.2%; accuracy of the model based on total mass and percentage solid mass was 75.6%. Conclusion: Computer-assisted 3D measurement of nodules at CT had good reproducibility and helped differentiate among subtypes of lung adenocarcinoma.

AB - Purpose: To identify the ability of computer-derived three-dimensional (3D) computed tomographic (CT) segmentation techniques to help differentiate lung adenocarcinoma subtypes. Materials and Methods: This study had institutional research board approval and was HIPAA compliant. Pathologically classified resected lung adenocarcinomas (n = 41) with thin-section CT data were identified. Two readers independently placed overinclusive volumes around nodules from which automated computer measurements were generated: mass (total mass) and volume (total volume) of the nodule and of any solid portion, in addition to the solid percentage of the nodule volume (percentage solid volume) or mass (percentage solid mass). Interobserver agreement and differences in measurements among pathologic entities were evaluated by using t tests. A multinomial logistic regression model was used to differentiate the probability of three diagnoses: invasive non-lepidic-predominant adenocarcinoma (INV), lepidic-predominant adenocarcinoma (LPA), and adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). Results: Mean percentage solid volume of INV was 35.4% (95% confidence interval [CI]: 26.2%, 44.5%)-higher than the 14.5% (95% CI: 10.3%, 18.7%) for LPA (P = .002). Mean percentage solid volume of AIS/MIA was 8.2% (95% CI: 2.7%, 13.7%) and had a trend toward being lower than that for LPA (P = .051). Accuracy of the model based on total volume and percentage solid volume was 73.2%; accuracy of the model based on total mass and percentage solid mass was 75.6%. Conclusion: Computer-assisted 3D measurement of nodules at CT had good reproducibility and helped differentiate among subtypes of lung adenocarcinoma.

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