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L. Pickup



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    MA 14 - Diagnostic Radiology, Staging and Screening for Lung Cancer I (ID 672)

    • Event: WCLC 2017
    • Type: Mini Oral
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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      MA 14.13 - Nodule Size Isn't Everything: Imaging Features Other Than Size Contribute to AI Based Risk Stratification of Solid Nodules (ID 8177)

      17:05 - 17:10  |  Author(s): L. Pickup

      • Abstract
      • Presentation
      • Slides

      Background:
      Previously proposed risk models for the malignancy of Indeterminate Pulmonary Nodules (IPNs) detected on Computed Tomography (CT) typically incorporate a mixture of clinical factors, such as age and smoking history, and radiological factors such as nodule size and location. Of the latter, size is considered one of the most significant. Artificial Intelligence based risk stratification software has been previously proposed that uses Texture Analysis with Machine Learning to predict IPN malignancy and has been shown to achieve high classification performance. While it is assumed that such techniques can capture image texture patterns that separate benignity from malignancy, such methods also intrinsically measure nodule size. The contribution of texture to classifier performance beyond size has not been studied and we seek to quantify this. We show, for the first time, the relative contributions of texture and size on the performance of Artificial Intelligence risk stratification of solid nodules.

      Method:
      Two datasets were created from the US National Lung Screening Trial (NLST). The first (A), comprising 640 solid nodules, was built to remove size as a discriminatory factor between benign and malignant; all malignant solid nodules between 4 and 20 mm diameter were selected, and for each, a benign solid nodule was selected that most closely matched it in diameter. Any malignant nodule for which an equivalently sized benign could not be found within 0.8 mm was rejected. Sizes were measured using automated volumetric segmentation. The second dataset (B), also comprising 640 subjects, included all malignant nodules in A but benign nodules were randomly selected following the empirical size distribution of the whole NLST dataset. Therefore, nodule size cannot be a discriminative factor in A but would be in B. Two nodule stratification algorithms were developed using Texture Analysis combined with Machine Learning (Support Vector Regression) integrating 20 variables including 3D Haralick, Gabor and Shape features, from A and B respectively using five-fold cross validation and the performance compared measuring Area-Under-the-Curve (AUC).

      Result:
      The average AUC for the algorithm trained on dataset A was 0.70 whereas using size alone on the same dataset gave an AUC of 0.50. The AUC was 0.91 for the algorithm trained on B.

      Conclusion:
      On this data, Texture Analysis with Machine Learning contributes 0.20 AUC points to classfication performance. Artificial Intelligence based risk classification can identify radiological features that are predictive of solid nodule malignancy that are independent of nodule size.

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    P1.05 - Early Stage NSCLC (ID 691)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Early Stage NSCLC
    • Presentations: 1
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      P1.05-008 - A Comparison of the Imaging Features of Early Stage Primary Lung Cancer in Patients Treated with Surgery, SABR and Microwave Ablation  (ID 9538)

      09:30 - 09:30  |  Author(s): L. Pickup

      • Abstract

      Background:
      Stereotactic Ablative Radiotherapy (SABR) and percutaneous microwave ablation (PMWA) are now being performed in patients deemed “medically inoperable” with non-small cell lung cancer (NSCLC). The majority of these patients are treated without ground truth histology, relying on imaging to establish the diagnosis. The purpose of this study was to investigate whether there were differences in the visible imaging features including CT Texture Analysis (CTTA) between patients referred for surgery, SABR and PMWA, which might suggest differences in underlying diagnosis.

      Method:
      A retrospective analysis of 92 patients with one pulmonary nodule (PN) suspected as T1N0M0 to T2AN0M0 NSCLC on imaging were treated either with SABR (22 patients), PMWA (25) or Video-assisted thorascopic surgery (45) of which 23 had NSCLC (SURG M) and 22 had benign disease (SURG B). Patient characteristics, CT nodule morphology, presence of emphysema and percentage emphysema score, FDG avidity and CT textural features were compared. Twenty texture features previously used in combination to predict nodule probability of malignancy were extracted from each automatic contoured region surrounding the PN. The Kruskal-Wallis test was used to compare texture features between the 4 patient groups (SABR, PMWA, SURG M and SURG B).

      Result:
      There was no significant difference in nodule morphology, volume at presentation (p=0.280) or nodule volume doubling times (p=0.149), and presence of emphysema (p= 0.348) or emphysema score (p= 0.367) between the 4 groups. There was no statistical difference in CTTA malignancy prediction score between the SABR, PMWA and SURG M groups (p>0.05). The probability of malignancy score was significantly lower (p-value < 0.01) for SURG B (0.58 mean ± 0.19 sd) vs. SABR (0.79 ± 0.15) treatment groups. In post-hoc analysis, 6 out of 20 texture features showed significant differences that were driven by the SURG B group.

      Conclusion:
      This is the first study to our knowledge to evaluate the radiological differences between patient groups referred for surgical and non-surgical treatments for NSCLC. On this small study, the results support the hypothesis that the non-operative patient groups comprise the same proportion of benign and malignant as those in the operative group. The results also demonstrate the potential clinical utility of CTTA in patient selection when histology is not obtainable. CTTA does not require volumetry detectable growth to detect change, and therefore may be a useful biomarker of malignancy at first diagnosis.