Approaches to Characterization of Indeterminate Lung Nodules Denise

Approaches to Characterization of Indeterminate Lung Nodules Denise

Approaches to Characterization of Indeterminate Lung Nodules Denise R. Aberle, MD Professor of Radiology and Bioengineering David Geffen School of Medicine at UCLA Overview Rationale and differences in approaches Semantic feature analysis Hand-crafted, quantitative features Deep learning Semantic feature analysis Visual characterization of object(s) using commonly understood terms & definitions Time tested Requires minimal software (DICOM viewer, calipers, ROI)

Agnostic to image acquisition/reconstruction parameters Highly labor intensive Not quantitative Moderate to high inter-reader variability Semantic features: The Brock model (screening) Developed and validated in separate screening cohorts Developed: PanCan | 1871 persons had 7008 nodules Validated: BCCA | 1090 persons had 5021 nodules Average of 5 to 7 nodules per person Combined clinical and imaging variables Both parsimonious & full models | AUC > 0.90

Nodules of all sizes (high number nodules < 4 mm) Brock Model (Parsimonious & Full) 9 Predictor Variables Parsimonious Model OR (95% CI) P value 1.91 (1.19-3.07) 0.008 Non-linear < 0.001 1.82 (1.12-2.98) 0.02 2.54 (1.45-4.43) 0.001 Age (Centered at 62; per year) Sex (Female vs. Male) FH (Yes vs. No) Emphysema (Yes vs. No) Nodule size (Centered at 4 mm) Nodule consistency:

GGN PSN Solid Lobe: Upper vs. other Nodule count per scan Spiculation (Yes vs. No) In test set: Parsimonious model AUC = 0.960 (0.927-0.980) M. NEJM 2013. McWilliams A etMcWilliams al. NEJM 2013;369:910-919. Brock Model (Parsimonious & Full) 9 Predictor Variables Parsimonious Model OR (95% CI) P value Full Logistic Regression OR (95% CI) P value 1.03 (0.99-1.07) 0.16 1.82 (1.12-2.97) 0.02

FH (Yes vs. No) 1.34 (0.83-2.17) 0.23 Emphysema (Yes vs. No) 1.34 (0.78-2.33) 0.29 Non-linear < 0.001 GGN 0.88 (0.48-1.62) 0.68 PSN 1.46 (0.74-2.88) 0.28 Solid Reference Age (Centered at 62; per year)

Sex (Female vs. Male) 1.91 (1.19-3.07) Nodule size (Centered at 4 mm) Nodule consistency: Lobe: Upper vs. other Non-linear 1.82 (1.12-2.98) 0.008 < 0.001 0.02 Nodule count per scan Spiculation (Yes vs. No) 2.54 (1.45-4.43) 0.001 1.93 (1.14-3.27) 0.02 0.92 (0.85-1.00) 0.049

2.17 (1.16-4.05) 0.02 In test set: Parsimonious model AUC = 0.960 (0.927-0.980) | Full model AUC = 0.970 (0.945-0.986) M. NEJM 2013. McWilliams A etMcWilliams al. NEJM 2013;369:910-919. Diagnostic prediction in screening setting | NLST CT arm of NLST using Baseline positive screens (N = 11,128 nodules in 6726 participants) At nodule level (4-30 mm): Predict lung cancer in same lobe as nodule Logistic regression with 10-fold cross-validation AUC = 0.89 (0.87-09.03) using clinical + imaging Variable OR

95% CI P-value Age (per 1 year increase) 1.047 1.023 1.071 < 0.0001 Sex: female vs. male 1.277 1.000 - 1.631 0.0500 Family history of lung cancer 1.312 1.004 1.714 0.0469 Body mass index (per 1 unit) 0.972 0.947 0.997

0.0285 Smoking status (current vs. former) 1.226 0.960 - 1.564 0.1018 Pack-years (per 1 pack-year) 1.011 1.006 1.015 < 0.0001 Self-reported history of COPD 0.928 0.600 1.435 0.7368 Longest diameter (per 1 mm increment) 1.148 1.125 1.171 < 0.0001

Consistency: GGN vs. solid nodule 0.654 0.479 0.894 0.0077 Consistency: PSN vs. solid nodule 1.011 0.663 1.541 0.9605 Upper vs. middle lobe 3.815 2.451 5.937 < 0.0001 Upper vs. lower lobe 1.671 1.306 2.137 < 0.0001 Margins: spiculated/ill defined vs. smooth

2.796 2.132 3.667 < 0.0001 Nodule count per scan (per additional nodule) 0.897 0.823 0.978 0.0140 Nodule-specific features Radiologist level of suspicion for lung cancer (high/moderately high vs. low/no) 9.739 5.491 17.271 < 0.0001 Limitations of semantic analysis Time intensive Low to moderate reader agreement Solid vs. subsolid nodules | K = 0.61

Solid vs. PSN vs. GGN | K = 0.33 Margin characteristics Size variations depending upon perception of nodule boundary Yildririm A. Eur Congress Radiol; 03-2013. Van Riel S. RSNA 2013. Illustrated semantic atlas Textual definitions & representative images with consensus by thoracic radiologists General features: Size | anatomic location Consistency Margins (Conspicuity and Type) Shape features

Internal features External (Peri-nodule surround) features Consistency: Peri-cystic Margins Serrated/Spiculated Peri-nodule Stretching | Paracicatricial emphysema Progress Nearing completion of atlas Reader study Atlas naive radiologists characterize nodules pre- and post-training Goal: Determine degree of convergence on semantic labels

Determine robust semantic features Semantic analysis Make understandable the outputs of quantitative and machine learning features Publish atlas Quantitative hand-crafted features Quantitative | Reproducible on same analytical platform Requires dedicated analytical SW | Changes workflow Challenge of standardization across platforms Affected by segmentation

Hounsfield intensity value resampling (quantization) Mathematical formulae may be implemented differently Some quantitative features sensitive to technique Acquisition: kV | mAs Reconstruction: Spatial resolution (slice thickness) | Algorithm | Kernel Different groups are publishing independent analyses Limited multicenter efforts to compare results across common datasets Radiomics: Hand-crafted features Converts images to higher dimensional data Quantitative features are extracted from segmentations Frequency +1

-1 +1 Frequency -1 Gillies RJ. Radiology 2016; 278: 563-577/ Effects of dose, kernel, and reconstruction on histogram Homogeneous Water Phantom Lung nodule segmentation B10f B45f Diagnostic dose B70f 100% 30% 10% B10f B45f Low dose dose B70f

100% 30% 10% UPPER: Effect of three different kernels at diagnostic dose (17.1 mGy) LOWER: Effect of three different kernels at low dose (3.8 mGy). All images use filtered back projection (FBP). UPPER: Effect of three different doses using b45f kernel and FBP. LOWER: Effect of three different doses using I44f and IR. Lo P et al. Med Phys 2016; 43(8):4854-4865. Semantic features in screen-eligible patients Consecutive individuals undergoing percutaneous lung biopsy (80%) or being followed for indeterminate nodules (20%) Age 50 | ever smokers | 6-27 mm mean diameter | FU > 2 years N = 146 | 50 benign, 90 NSCLC, 6 well differentiated neuroendocrine Parsimonious logistic regression model Clinical & Semantic Features

OR 95% CI P-value Age (per 1 year increment) 1.061 1.007-1.118 0.027 Nodule size per mm 1.12 1.053-1.211 0.001 Consistency (sub-solid vs. solid) 3.579 1.162-11.020 0.026 Margin (non-smooth vs. smooth) 3.538

1.201-10.424 0.022 Radiologist suspicion 3.050 1.942-4.791 0.000 NPV = 86.84 | PPV = 84.26 | AUC = 0.90 (95% CI = 0.83-0.95) Quantitative feature analysis Consecutive individuals undergoing percutaneous lung biopsy (80%) or being followed for indeterminate nodules (20%) Segmentation: Automated, semi-automated, manual 1000 18 features (sequential forward floating selection with 20-fold cross-over sampling) 5 texture features using logistic regression with back feature selection Quantitative Features OR 95% CI P-value QMBLobness 40_skewness -1.997

-3.178- -0.816 0.001 QMLyy10_skewness 1.547 0.447-2.646 0.006 QMSurfaceFrobeniusNorm10_kurtosis 0.619 0.170-1.069 0.007 QMSurfaceGlcm32InfoCorrelationA 24.015 13.088-34.942 0.000 QMSurfaceLz10_skewness -2.189 0.049 -4.371- -0.007

NPV = 73.81 | PPV = 81.73 | AUC = 0.86 (95% CI = 0.77-0.91) Semantic and quantitative performance Deep learning using CNNs Nonlinear alternative to ad hoc descriptors for pattern recognition Input is the image Output is prediction of indolent vs. aggressive | benign vs. malignant Learns from the raw data itself: minimal preprocessing Not dependent on segmentation Relatively agnostic to image acquisition/reconstruction Requires LARGE data sets Several different architectures | transfer learning Data augmentation and scaling Data augmentation by generating

orthogonal images as inputs into CNN Scaling used to identify local pixel attenuations as well as larger context. Scales here are all 64 x 64 at 10, 20, 40 mm Ciompi F et al. Nature Scientific Reports 2017; 7:46479 | DOI: 10.1038/srep46479 CNN architecture Fully connected layer Ciompi F et al. Nature Scientific Reports 2017; 7:46479 | DOI: 10.1038/srep46479 Using semantic features for learning Train CNN with semantic labels to make output more intuitive to humans Assess classification performance for semantic labels & diagnostic prediction Comparison: 3D hierarchical semantic network AUC = 0.856 ( 0.026) 3D single CNN AUC = 0.847 ( 0.024) Mean difference = 0.005 (95% CI: 0.0051-0.0129); p = 0.009 Will Hsu, PhD

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