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Pingfu Fu



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    MA15 - Usage of Computer and Molecular Analysis in Treatment Selection and Disease Prognostication (ID 141)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Pathology
    • Presentations: 1
    • Now Available
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      MA15.05 - Computerized Measurements of Cellular Diversity on H&E Tissue Are Prognostic of OS and Associated with Mutational Status in NSCLC (Now Available) (ID 1975)

      15:45 - 17:15  |  Author(s): Pingfu Fu

      • Abstract
      • Presentation
      • Slides

      Background

      Tumor heterogeneity is known to be implicated in chemotherapeutic resistance and poor prognosis for non-small cell lung cancer (NSCLC). In this study we sought to evaluate the role of computer extracted features reflecting the intrinsic cellular morphological diversity (ICMD) of tumors from digitized H&E stained images of early-stage NSCLC patients. Additionally, we sought to evaluate the association of these ICMD features in adenocarcinomas with the ALK and EGFR mutational status.

      Method

      Two cohorts, D1 and D2, of digitized H&E stained tissue microarray images (TMA) of NSCLC, n=395 and n=91, respectively, were used for modeling the ICMD predictor. A pretrained deep learning model was used for segmentation of nuclei, and clusters of proximally located nuclei were identified. The ICMD features were then extracted as the variations in shape, size, and texture measurements of nuclei within the clusters. A Cox proportional hazard model using the ICMD features was then trained for lung adenocarcinomas (LUAD, n=270), and squamous cell carcinomas (LUSC, n=216), separately, and was validated on independent cohort from (D3) The Cancer Genome Atlas (TCGA) (n=473) to predict Overall Survival (OS). Univariate and multivariate analyses were performed on (D3).

      Result

      In (D3), high risk patients predicted by the ICMD features had significantly poorer survival (HR (95% CI) = 1.48 (1.06-2.06), p=0.021 for LUSC, HR (95% CI) = 1.59 (1.11-2.29), p=0.006 for LUAD) in univariate analysis. In multivariate analysis, controlling for major clinical variables, ICMD was independently associated with 5-year OS (p<0.016). (See Table 1) We also found that ICMD features were associated with driver mutations ALK (p=0.0204) and EGFR (p=0.0017) in LUAD.

      Table 1| Multivariate analysis for overall survival on the validation set D3.

      Multivariate Cox Proportional Hazard Model Analysis Controlling for Other Variables

      TCGA-LUSC

      TCGA-LUAD

      Variable

      HR (95% CI)

      p value

      HR (95% CI)

      p value

      Age (>65 vs <=65)

      1.14(0.81-1.61)

      0.451

      0.89(0.63-1.28)

      0.540

      Smoking status

      1.36(0.83-2.23)

      0.221

      1.14(0.64-2.01)

      0.661

      Overall Stage (Stage II vs I)

      1.13(0.66-1.94)

      0.651

      1.86(1.04-3.32)

      0.037

      T-Stage (T2,3 vs T1)

      1.26(0.85-1.87)

      0.244

      1.25(0.85-1.85)

      0.263

      N-Stage (N1 vs N0)

      1.36(0.77-2.41)

      0.292

      3.11(1.55-6.23)

      0.001

      Developed Model

      High risk vs. Low risk

      1.52(1.08-2.13)

      0.016

      1.55(1.09-2.22)

      0.015

      CI = 95% confidence interval; HR = Mantel-Haenszel Hazard ratio. Values in bold are statistically significant, p<=0.05.

      Conclusion

      Computer extracted image features of cellular diversity were able to predict OS in NSCLC and were also associated with the ALK and EGFR mutational status. Future work will entail evaluating ICMD features in predicting added benefit of adjuvant therapy in early stage NSCLCs as well as correlating with gene expression data.

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    P1.04 - Immuno-oncology (ID 164)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Immuno-oncology
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.04-25 - CT Based Vessel Tortuosity Features Are Prognostic of Overall Survival and Predictive of Immunotherapy Response in NSCLC Patients (ID 826)

      09:45 - 18:00  |  Author(s): Pingfu Fu

      • Abstract

      Background

      Recently majority of patients with advanced non-small cell lung cancer (NSCLC) without targetable mutations are treated with immune checkpoint inhibitors (ICI). Since there are currently no validated biomarkers for predicting benefit of immunotherapy (IO), there is an unmet clinical need for development of such biomarkers. The tumor vasculature is a key component of the tumor micro-environment that can influence its behavior and therapeutic refractoriness. We aimed to evaluate the prognostic and predictive potential of quantitative vessel trotuosity (QVT), in the NSCLC patients treated with ICI drugs. Two hypotheses were established: first, the QVT on pre-treatment CT scans of NSCLC patients are associated with overall survival (OS). Second, the prognostic QVT features can lead to identify the patients who will benefit from IO.

      Method

      This study include 128 patients with advanced NSCLC. All patients underwent a baseline contrast CT imaging. Patients who did not receive IO drugs after 2 cycles due to a lack of response or progression as per RECIST were classified as non-responders. The dataset was splitted into a discovery (N=64) and validation sets (N=64). A set of 74 QVT features pertaining to tortuosity and curvature of tumor vasculature was extracted in CT scans. The initial set of QVT features were reduced to 8 features using least absolute shrinkage and selection operator (LASSO) in conjunction with OS data of the patients. Then, cox proportional hazard model was used to determine the contribution of each feature for categorizing survival groups. The weighted sum of selected 8 features gave a risk score (QRS) per patient. Patients in validation set were stratified based on QRS using the cutoff and feature weights learned in the discovery set. Prognostic features in conjunction with a linear discriminant machine learning model and OS were used to build a model to predict the response to IO. The prognostic features were also used for unsupervised clustering of the patients.

      Result

      The QRS risk score was able to stratify patients into two survival groups in validation set (Fig1. a-b) with p-value=0.022, Hazard ratio (HR)=0.47 and concordance index (CI)=0.61. The response prediction model yielded an AUC of 0.64±0.03 (Fig1.c). The agreement between patients with high OS and positive response to therapy was found to be 0.62 on unsupervised clustering method (Fig1. d).

      fig1.png

      Conclusion

      The CT extracted QVT features was found to be prognostic of OS and also showed predictive value that could be used to identify patients who will benefit from IO.

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    P2.04 - Immuno-oncology (ID 167)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Immuno-oncology
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.04-16 - Novel CT Based Radiomic Features are Prognostic and Predictive of Benefit of Chemoimmunotherapy in Advanced Non-Squamous NSCLC (ID 2769)

      10:15 - 18:15  |  Author(s): Pingfu Fu

      • Abstract

      Background

      Carboplatin, pemetrexed and pembrolizumab (C/P/P) is currently approved for patients with advanced non-squamous carcinoma of the lung (NS-NSCLC) based on superior survival outcomes noted in KEYNOTE-189. Since clinical benefit was observed across all PD-L1 expression categories, there are currently no robust predictive biomarkers that can identify subsets of patients likely to derive benefit from this regimen. We sought to evaluate whether radiomic features extracted from within and outside the nodule on pre-therapy CT scans could predict response to C/P/P.

      Method

      We retrospectively identified 52 patients with stage IV NS-NSCLC who received C/P/P. Of these, 6 were excluded because of non-evaluable thoracic lesions. Lung tumors were contoured on 3D SLICER software by an expert reader. Textural and shape radiomic features were extracted from intra/peritumoral regions using MATLAB® 2018b platform (Mathworks, Natick, MA). The primary endpoint of our study was RECIST response and secondary end point was overall survival (OS). A linear discriminant analysis classifier (LDA) was used to predict response across 100 iterations of threefold cross validation in the dataset. Performance of classifier on response was measured by area under receiver operating characteristic curve (AUC). To build the multivariate radiomic signature for OS, least absolute shrinkage and selection operator (LASSO) Cox regression model was used and a risk score was computed according to a linear combination of selected features. Patients were divided into high-risk or low-risk groups based on median risk score.

      Result

      The top five radiomic features (intra/peritumoral textural patterns) predictive of response to C/P/P were identified by mRMR feature selection method. LDA classifier using these features could discriminate responders from non-responders with an AUC of 0.77 ± 0.05.

      The radiomic risk score was calculated using a linear combination of top six selected features from LASSO with corresponding coefficients. In a multivariate Cox proportional hazards model using a combination of clinicopathologic and radiomic features, the radiomics signature was found to be significantly associated with OS (averaged on 100 iteration of CV) (HR 10.42; 95% CI: 4.18-26; P = 4.92e-07). Kaplan-Meier survival analyses according to the radiomics signature risk-score showed significantly worse survival in the high risk category.

      Conclusion

      Textural features within and outside the nodule on pre-treatment CT images of patients with NS-NSCLC treated with C/P/P were predictive of responses and OS. Additional validation of these quantitative image-based biomarkers in independent cohorts is warranted.

      p1.png

      Figure: Kaplan-Meier survival analyses of patients (N = 46) with NS-NSCLC treated with C/P/P using the radiomics signature risk-score.

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    P2.12 - Small Cell Lung Cancer/NET (ID 180)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Small Cell Lung Cancer/NET
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.12-03 - Building and Validating a Lymphocyte Nadir Based Model to Predict Survival in Patients with Limited Stage-Small Cell Lung Cancer (ID 2931)

      10:15 - 18:15  |  Author(s): Pingfu Fu

      • Abstract

      Background

      Increasing evidence indicates host immunity participate in cancer progression and metastases. It has been reported that the lymphocyte nadir is an independent prognostic marker for survival in various cancers. We have previously reported a survival significance of baseline lymphocyte in limited stage small cell lung cancer (LS-SCLC). Here we hypothesize that treatment induced lymphopenia, like the lymphocyte nadir during the course of treatment, in combination of clinical factors can predict survival better than conventional models in patients with LS-SCLC.

      Method

      This is a retrospective study of 616 patients from a single Institution. Consecutive patients with LS- SCLC treated with thoracic radiation (with or without concurrent chemotherapy) from 2013 to 2017 were included. Additional eligibility included availability of complete-blood-count data from baseline and at least two time points during the course of radiotherapy. These 616 patients were randomly divided into a training dataset (n=308) and a validation dataset (n=308). The primary endpoint was overall OS. Univariate proportional hazard (PH) cox model was used to assess potential clinicopathological predictors on OS. The multivariable Cox PH model was constructed by the forward selection. According to the final Cox model built using significant variables from training dataset, we calculated the risk score for every patient and validate the predictive valuable of the risk score on OS in the validation set.

      Result

      Under univariate analysis, younger age (HR 1.02 per 1 yr, 95%CI 1.006-1.043, p=0.008), female gender (HR 1.40, 95%CI 0.95-2.07, p=0.09), earlier stage (stage I-II vs stage III, HR 2.2, 95%CI 1.11-4.33, p=0.02), concurrent chemotherapy (concurrent vs. not, HR 0.61, 95%CI 0.42-0.88, p=0.01) and a higher lymphocyte nadir ( HR 0.48, 95% CI 0.20-1.14, per 103 lymphocytes/ μL, p=0.097) was significantly associated with increased OS in the training dataset. Using lymphocyte nadir in combination of significant clinical factors from univariate analysis, we developed a multivariable Cox PH model (lymphocyte nadir: HR 0.39, 95% CI 0.16-0.99, per 103 lymphocytes/ μL, p=0.048) with concordance (C)-index of 0.63. In the validation dataset, the multivariable model revealed that lymphocyte nadir had a borderline significance on OS (HR 0.45, 95% CI 0.19-1.06, per 103 lymphocytes/ μL, p=0.067) with a comparable c-index of 0.60. Moreover, the risk score calculated using the coefficients from the final Cox model built using the training dataset remained to be a significant predictor for OS (HR 2.04, 95% CI 1.36-3.07, per 1 risk score increase, p<0.0001) in validation dataset.

      Conclusion

      This may be the first study validated a survival predictive model based on lymphocyte nadir in a large sample of patients with LS-SCLC. Should it should be validated in an external dataset, this model might provide some prediction for each patient and provide an opportunity to individualize treatment based on the individual’s survival probability.

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    P2.17 - Treatment of Early Stage/Localized Disease (ID 189)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Treatment of Early Stage/Localized Disease
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.17-34 - Integrated Clinico-Radiomic Nomogram for Predicting Disease-Free Survival (DFS) in Stage I and II Non-Small Cell Lung Cancer (ID 2886)

      10:15 - 18:15  |  Author(s): Pingfu Fu

      • Abstract

      Background

      Early stage non-small cell lung cancer (ES-NSCLC) comprises about 45% of all NSCLC patients, with 5-year survival ranging between 30-49%. Surgical resection is the standard of care curative modality in these patients but about 30-55% of patients often recur following surgery within the first 3 years. There is currently no validated method to stratify patients based on their risk of recurrence following surgery in these patients. In this project, we develop and validate a nomogram using a combination of CT-derived radiomic textural features and clinco-pathologic factors, in order to predict DFS in ES-NSCLC.

      Method

      This study comprised 350 ES-NSCLC patients from two different institutions who underwent surgery (75 patients relapsed). Radiomic textural features were extracted from tumor region (Intratumoral - IT) as well as from the annular ring shaped peritumoral region (PT) with 3mm as a ring thickness and extending 9 mm outside the nodule. A total of 124 features from Gabor, Laws, Laplace, Haralick and Collage feature families were extracted from IT and each PT ring for all patients. The most stable, significant and uncorrelated features were selected from D1 (N=221) and used to build a Lasso-regularized multivariate Cox-regression model to generate a Radiomic Risk Score (RRS) derived from weighted Lasso coefficients. Further, RRS was integrated with clinic-pathologic variables (Lympho-vascular invasion LVI and AJCC stage) which were independently predictive on DFS in multivariate analysis to build a clinical-radiomics score (CRS). A nomogram was constructed to visually assess the CRS and RRS on DFS. Performances were evaluated using hazard ratios (HR), concordance index (C-Index) along with decision and calibration curves to show the differences between the individual and integrated risk scores.

      Result

      Top 14 radiomic features included 6 from IT and 8 from 0-9 mm PT distance. The constructed RRS could predict DFS (n=221, C-index=0.69, HR = 3.8; 95% CI- 2.7-5.6, p<0.05) on training (D1) and (n=129, C-index=0.69, HR – 2.5; 95% CI – 1.8-4.7; p<0.05) on a blinded validation cohort (D2), Addition of LVI and pN to build the CRS, increased C-index to 0.74, (p<0.05). Decision and calibration curve analysis shows improved performance of CRS over RRS or clinco-pathologic factors alone.

      imagefinal.jpg

      Conclusion

      Addition of prognostic clinical factors (LVI, AJCC stage) improved the performance of the Radiomic Risk Score model in order to accurately predict DFS in ES-NSCLC patients undergoing surgery.