Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test-retest, the biological range and a feature independence measure. There were 66 (30.14%) features with concordance correlation coefficient ≥ 0.90 across test-retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R (2) Bet ≥ 0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91% for a size-based feature and 92% for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test-retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.

A single click ensemble segmentation (SCES) approach based on an existing “Click&Grow” algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76% respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.

Our findings indicate that measurement of changes in tumor volumes is adequately reproducible. Using tumor volumes as the basis for response assessments could have a positive impact on both patient management and clinical trials. More authoritative work to qualify or discard changes in volume as the basis for response assessments should proceed.

Purpose To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC). Materials and Methods For twenty NSCLC patients (stage Ib – IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data is available on www.cancerdata.org. Results High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5 ± 9.0, mean ± SD) and union (94.2 ± 6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4 ± 83.2 cm3, mean ± SD) and manual delineations (81.9 ± 94.1 cm3; p = 0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r = 0.96). Conclusion Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered the “gold standard”. This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors.

BackgroundImmuno-oncology and cancer immunotherapies are areas of intense research. The numbers and locations of CD8+ tumor-infiltrating lymphocytes (TILs) are important measures of the immune response to cancer with prognostic, pharmacodynamic, and predictive potential. We describe the development, validation, and application of advanced image analysis methods to characterize multiple immunohistochemistry-derived CD8 parameters in clinical and nonclinical tumor tissues.MethodsCommercial resection tumors from nine cancer types, and paired screening/on-drug biopsies of non–small-cell lung carcinoma (NSCLC) patients enrolled in a phase 1/2 clinical trial investigating the PD-L1 antibody therapy durvalumab (NCT01693562), were immunostained for CD8. Additional NCT01693562 samples were immunostained with a CD8/PD-L1 dual immunohistochemistry assay. Whole-slide scanning was performed, tumor regions were annotated by a pathologist, and images were analyzed with customized algorithms using Definiens Developer XD software. Validation of image analysis data used cell-by-cell comparison to pathologist scoring across a range of CD8+ TIL densities of all nine cancers, relying primarily on 95% confidence in having at least moderate agreement regarding Lin concordance correlation coefficient (CCC = 0.88–0.99, CCC_lower = 0.65–0.96).ResultsWe found substantial variability in CD8+ TILs between individual patients and across the nine types of human cancer. Diffuse large B-cell lymphoma had several-fold more CD8+ TILs than some other cancers. TIL densities were significantly higher in the invasive margin versus tumor center for carcinomas of head and neck, kidney and pancreas, and NSCLC; the reverse was true only for prostate cancer. In paired patient biopsies, there were significantly increased CD8+ TILs 6 weeks after onset of durvalumab therapy (mean of 365 cells/mm2 over baseline; P = 0.009), consistent with immune activation. Image analysis accurately enumerated CD8+ TILs in PD-L1+ regions of lung tumors using the dual assay and also measured elongate CD8+ lymphocytes which constituted a fraction of overall TILs.ConclusionsValidated image analysis accurately enumerates CD8+ TILs, permitting comparisons of CD8 parameters among tumor regions, individual patients, and cancer types. It also enables the more complex digital solutions needed to better understand cancer immunity, like analysis of multiplex immunohistochemistry and spatial evaluation of the various components comprising the tumor microenvironment.Trial registrationClinicalTrials.gov identifier: NCT01693562.Study code: CD-ON-MEDI4736–1108.Interventional study (ongoing but not currently recruiting).Actual study start date: August 29, 2012.Primary completion date: June 23, 2017 (final data collection date for primary outcome measure).Electronic supplementary materialThe online version of this article (10.1186/s40425-018-0326-x) contains supplementary material, which is available to authorized users.

Background. This study presents a semiautomated approach for volumetric analysis of lung tumors and evaluates the feasibility of using volumes as an alternative to line lengths as a basis for response evaluation criteria in solid tumors (RECIST). The overall goal for the implementation was to accurately, precisely, and efficiently enable the analyses of lesions in the lung under the guidance of an operator. Methods. An anthropomorphic phantom with embedded model masses and 71 time points in 10 clinical cases with advanced lung cancer was analyzed using a semi-automated workflow. The implementation was done using the Cognition Network Technology. Results. Analysis of the phantom showed an average accuracy of 97%. The analyses of the clinical cases showed both intra- and interreader variabilities of approximately 5% on average with an upper 95% confidence interval of 14% and 19%, respectively. Compared to line lengths, the use of volumes clearly shows enhanced sensitivity with respect to determining response to therapy. Conclusions. It is feasible to perform volumetric analysis efficiently with high accuracy and low variability, even in patients with late-stage cancer who have complex lesions.

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3079 Background: Tumors use multiple means of immune evasion, notably the programmed death-1 (PD1)/PDL1 pathway. Anti-PD1/PDL1 therapy induces anti-tumor activity and has improved pt outcomes. Activation of the immunosuppressive CD39/CD73/adenosine pathway might play a role in pts who do not benefit from anti-PD1/PDL1 therapies. We evaluated expression of CD73 and PDL1 and explored the association between CD73 and intraepithelial (IE) CD8+ cells (TILs) to begin to understand their potential interplay in cancer. Methods: Immunohistochemistry for PDL1, CD73 and CD8 was conducted on tumors of non-squamous NSCLC (NSq) (n=42), GE (n=50), and UBC (n=50). PDL1 and CD73 were scored by image analysis with Definiens software. IE CD8+ TILs were scored semi-quantitatively by a pathologist (0-2 = low; 3-4 = high). Using the top tertile of PDL1 and CD73 for high expression levels, a Fisher’s meta-analysis was calculated across the three indications. Results: Across all tumors, 25% (35/142) were PDL1 high (+), but CD73 low (-) and another 25% (35/142) were CD73+ but PDL1- (p=0.06, see table). This trend for mutually exclusive high expression of PDL1/CD73 was strongest in GE (p<0.01). In the PDL1+ group 76% (35/46) had high IE CD8+ TILs whereas in the CD73+ group only 35% (16/46) had high TILs (p<0.0001 using a proportions test). In the PDL1+/CD73- pt subset 77% (27/35) were CD8+ high vs only 23% (8/35) in the PDL1-/CD73+ subset. Conclusions: The identification of distinct pt subsets based on high PDL1 and/or CD73 expression suggests that tumors have multiple mechanisms of immune evasion. Increased IE CD8+ TILs were associated with PDL1 expression. The finding that PDL1-/ CD73+ tumors have lower IE CD8+ TILs compared to PDL1+/CD73- tumors suggests a role for CD73 in excluding IE TILs. Larger sample sets are needed to confirm these findings and to further explore any relationship with the tumor microenvironment. Our data suggests potential approaches to identify subsets of pts likely to benefit from immunotherapy targeting PDL1 and CD73. [Table: see text]