Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

Saad, Maliazurina; Lee, Ik Hyun; Choi, Tae‐Sun

doi:10.1002/ima.22336

“…Demographics, histology and pathological staging are among clinical indicators that have been proven in the literature [34][35][36], hence previous works on predicting survival among NSCLC patients are concentrated on mixing these readily available clinical factors with AUCs range between 0.62-0.79 [19][20][27][28]. To the best of our knowledge, this is the first study establishing the fusion of both clinical with imaging covariates, which has been proven to better predict survival (AUCs between 0.77 and 0.97).…”

Section: Discussionmentioning

confidence: 99%

“…Tumor Delineation Figure 1 outlines the processes required to achieve the objectives of this study. Tumor delineation was a pre-processing step and was performed using an automated tool developed using geometrical and topological processing to facilitate this process [27]. It eliminates the manual delineation work required in this study.…”

Section: The Cohort Consists Of 211 Subjects That Underwent Both Compmentioning

confidence: 99%

Leveraging hybrid biomarkers in clinical endpoint prediction

Saad

¹

,

Lee

²

2020

Preprint

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Background Clinical endpoint prediction remains challenging for health providers. Although predictors such as age, gender, and disease staging are of considerable predictive value, the accuracy often ranges between 60% and 80%. An accurate prognosis assessment is required for making effective clinical decisions. Methods We proposed an extended prognostic model based on clinical covariates with adjustment for additional variables that were radio-graphically induced, termed imaging biomarkers. Eight imaging biomarkers were introduced and investigated in a cohort of 68 non-small cell lung cancer subjects with tumor internal characteristic. The subjects comprised of 40 males and 28 females with mean age at 68.7 years. The imaging biomarkers used to quantify the solid component and non-solid component of a tumor. The extended model comprises of additional frameworks that correlate these markers to the survival ends through uni- and multi-variable analysis to determine the most informative predictors, before combining them with existing clinical predictors. Performance was compared between traditional and extended approaches using Receiver Operating Characteristic (ROC) curves, Area under the ROC curves (AUC), Kaplan-Meier (KM) curves, Cox Proportional Hazard, and log-rank tests (p-value). Results The proposed hybrid model exhibited an impressive boosting pattern over the traditional approach of prognostic modelling in the survival prediction (AUC ranging from 77– 97%). Four developed imaging markers were found to be significant in distinguishing between subjects having more and less dense components: (P = 0.002–0.006). The correlation to survival analysis revealed that patients with denser composition of tumor (solid dominant) lived 1.6–2.2 years longer (mean survival) and 0.5–2.0 years longer (median survival), than those with less dense composition (non-solid dominant). Conclusion The present study provides crucial evidence that there is an added value for incorporating additional image-based predictors while predicting clinical endpoints. Though the hypotheses were confirmed in a customized case study, we believe the proposed model is easily adapted to various clinical cases, such as predictions of complications, treatment response, and disease evolution.

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“…Demographics, histology and pathological staging are among clinical indicators that have been proven in the literature [34][35][36], hence previous works on predicting survival among NSCLC patients are concentrated on mixing these readily available clinical factors with AUCs range between 0.62-0.79 [19,20,27,28]. To the best of Table 8 Mean and median survival as calculated from Kaplan-Meier survival curves.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 1 outlines the processes required to achieve the objectives of this study. Tumor delineation was a pre-processing step and was performed using an automated tool developed using geometrical and topological processing to facilitate this process [27]. It eliminates the manual delineation work required in this study.…”

Section: Clinical Materialsmentioning

confidence: 99%

Leveraging hybrid biomarkers in clinical endpoint prediction

Saad

¹

,

Lee

²

2020

BMC Med Inform Decis Mak

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1

0

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Background Clinical endpoint prediction remains challenging for health providers. Although predictors such as age, gender, and disease staging are of considerable predictive value, the accuracy often ranges between 60 and 80%. An accurate prognosis assessment is required for making effective clinical decisions. Methods We proposed an extended prognostic model based on clinical covariates with adjustment for additional variables that were radio-graphically induced, termed imaging biomarkers. Eight imaging biomarkers were introduced and investigated in a cohort of 68 non-small cell lung cancer subjects with tumor internal characteristic. The subjects comprised of 40 males and 28 females with mean age at 68.7 years. The imaging biomarkers used to quantify the solid component and non-solid component of a tumor. The extended model comprises of additional frameworks that correlate these markers to the survival ends through uni- and multi-variable analysis to determine the most informative predictors, before combining them with existing clinical predictors. Performance was compared between traditional and extended approaches using Receiver Operating Characteristic (ROC) curves, Area under the ROC curves (AUC), Kaplan-Meier (KM) curves, Cox Proportional Hazard, and log-rank tests (p-value). Results The proposed hybrid model exhibited an impressive boosting pattern over the traditional approach of prognostic modelling in the survival prediction (AUC ranging from 77 to 97%). Four developed imaging markers were found to be significant in distinguishing between subjects having more and less dense components: (P = 0.002–0.006). The correlation to survival analysis revealed that patients with denser composition of tumor (solid dominant) lived 1.6–2.2 years longer (mean survival) and 0.5–2.0 years longer (median survival), than those with less dense composition (non-solid dominant). Conclusion The present study provides crucial evidence that there is an added value for incorporating additional image-based predictors while predicting clinical endpoints. Though the hypotheses were confirmed in a customized case study, we believe the proposed model is easily adapted to various clinical cases, such as predictions of complications, treatment response, and disease evolution.

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“…124 Figure 1 outlines the processes required to achieve the objectives of this study. Tumor 125 delineation was a pre-processing step and was performed using an automated tool developed using geometrical and topological processing to facilitate this process [27]. It eliminates the manual 127 delineation work required in this study.…”

Section: Tumor Delineationmentioning

confidence: 99%

Leveraging hybrid biomarkers in clinical endpoint prediction

Saad

¹

,

Lee

²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background Clinical endpoint prediction remains challenging for health providers. Although predictors such as age, gender, and disease staging are of considerable predictive value, the accuracy often ranges between 60% and 80%. An accurate prognosis assessment is required for making effective clinical decisions. Methods We proposed an extended prognostic model based on clinical covariates with adjustment for additional variables that were radio-graphically induced, termed imaging biomarkers. Eight imaging biomarkers were introduced and investigated in a cohort of 68 non-small cell lung cancer subjects with tumor internal characteristic. The subjects comprised of 40 males and 28 females with mean age at 68.7 years. The imaging biomarkers used to quantify the solid component and non-solid component of a tumor. The extended model comprises of additional frameworks that correlate these markers to the survival ends through uni- and multi-variable analysis to determine the most informative predictors, before combining them with existing clinical predictors. Performance was compared between traditional and extended approaches using Receiver Operating Characteristic (ROC) curves, Area under the ROC curves (AUC), Kaplan-Meier (KM) curves, Cox Proportional Hazard, and log-rank tests (p-value). Results The proposed hybrid model exhibited an impressive boosting pattern over the traditional approach of prognostic modelling in the survival prediction (AUC ranging from 77– 97%). Four developed imaging markers were found to be significant in distinguishing between subjects having more and less dense components: (P = 0.002–0.006). The correlation to survival analysis revealed that patients with denser composition of tumor (solid dominant) lived 1.6–2.2 years longer (mean survival) and 0.5–2.0 years longer (median survival), than those with less dense composition (non-solid dominant). Conclusion The present study provides crucial evidence that there is an added value for incorporating additional image-based predictors while predicting clinical endpoints. Though the hypotheses were confirmed in a customized case study, we believe the proposed model is easily adapted to various clinical cases, such as predictions of complications, treatment response, and disease evolution.

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Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

Cited by 6 publications

References 46 publications

Leveraging hybrid biomarkers in clinical endpoint prediction

Leveraging hybrid biomarkers in clinical endpoint prediction

Leveraging hybrid biomarkers in clinical endpoint prediction

Leveraging hybrid biomarkers in clinical endpoint prediction

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