scholarly journals Automated tumor proportion scoring for PD-L1 expression based on multistage ensemble strategy in non-small cell lung cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Boju Pan ◽  
Yuxin Kang ◽  
Yan Jin ◽  
Lin Yang ◽  
Yushuang Zheng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Analysis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Automated Image Analysis ◽  
Small Cell Lung ◽  
Cutoff Values ◽  
Scoring Algorithm ◽  
Better Than

Abstract Introduction Programmed cell death ligand-1 (PD-L1) expression is a promising biomarker for identifying treatment related to non-small cell lung cancer (NSCLC). Automated image analysis served as an aided PD-L1 scoring tool for pathologists to reduce inter- and intrareader variability. We developed a novel automated tumor proportion scoring (TPS) algorithm, and evaluated the concordance of this image analysis algorithm with pathologist scores. Methods We included 230 NSCLC samples prepared and stained using the PD-L1(SP263) and PD-L1(22C3) antibodies separately. The scoring algorithm was based on regional segmentation and cellular detection. We used 30 PD-L1(SP263) slides for algorithm training and validation. Results Overall, 192 SP263 samples and 117 22C3 samples were amenable to image analysis scoring. Automated image analysis and pathologist scores were highly concordant [intraclass correlation coefficient (ICC) = 0.873 and 0.737]. Concordances at moderate and high cutoff values were better than at low cutoff values significantly. For SP263 and 22C3, the concordances in squamous cell carcinomas were better than adenocarcinomas (SP263 ICC = 0.884 vs 0.783; 22C3 ICC = 0.782 vs 0.500). In addition, our automated immune cell proportion scoring (IPS) scores achieved high positive correlation with the pathologists TPS scores. Conclusions The novel automated image analysis scoring algorithm permitted quantitative comparison with existing PD-L1 diagnostic assays and demonstrated effectiveness by combining cellular and regional information for image algorithm training. Meanwhile, the fact that concordances vary in different subtypes of NSCLC samples, which should be considered in algorithm development.

scholarly journals Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis

2019 ◽  
Vol 33 (3) ◽  
pp. 380-390 ◽  
Author(s):  
Moritz Widmaier ◽  
Tobias Wiestler ◽  
Jill Walker ◽  
Craig Barker ◽  
Marietta L. Scott ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Death ◽  
Image Analysis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Automated Image Analysis ◽  
Percentage Agreement ◽  
Small Cell Lung ◽  
Scoring Algorithm

Abstract Tumor programmed cell death ligand-1 (PD-L1) expression is a key biomarker to identify patients with non-small cell lung cancer who may have an enhanced response to anti-programmed cell death-1 (PD-1)/PD-L1 treatment. Such treatments are used in conjunction with PD-L1 diagnostic immunohistochemistry assays. We developed a computer-aided automated image analysis with customized PD-L1 scoring algorithm that was evaluated via correlation with manual pathologist scores and used to determine comparability across PD-L1 immunohistochemistry assays. The image analysis scoring algorithm was developed to quantify the percentage of PD-L1 positive tumor cells on scans of whole-slide images of archival tumor samples from commercially available non-small cell lung cancer cases, stained with four immunohistochemistry PD-L1 assays (Ventana SP263 and SP142 and Dako 22C3 and 28-8). The scans were co-registered and tumor and exclusion annotations aligned to ensure that analysis of each case was restricted to comparable tissue areas. Reference pathologist scores were available from previous studies. F1, a statistical measure of precision and recall, and overall percentage agreement scores were used to assess concordance between pathologist and image analysis scores and between immunohistochemistry assays. In total, 471 PD-L1-evalulable samples were amenable to image analysis scoring. Image analysis and pathologist scores were highly concordant, with F1 scores ranging from 0.8 to 0.9 across varying matched PD-L1 cutoffs. Based on F1 and overall percentage agreement scores (both manual and image analysis scoring), the Ventana SP263 and Dako 28-8 and 22C3 assays were concordant across a broad range of cutoffs; however, the Ventana SP142 assay showed very different characteristics. In summary, a novel automated image analysis scoring algorithm was developed that was highly correlated with pathologist scores. The algorithm permitted quantitative comparison of existing PD-L1 diagnostic assays, confirming previous findings that indicate a high concordance between the Ventana SP263 and Dako 22C3 and 28-8 PD-L1 immunohistochemistry assays.

scholarly journals 38 Clinical validation of an image analysis assay for determining programmed death-ligand 1 (22C3) in non-small cell lung cancer

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A39-A39
Author(s):  
Roberto Gianani ◽  
Will Paces ◽  
Elliott Ergon ◽  
Kristin Shotts ◽  
Vitria Adisetiyo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Analysis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Clinical Validation ◽  
Small Cell Lung ◽  
Programmed Death Ligand 1 ◽  
Programmed Death

BackgroundDetermination of programmed death-ligand 1 (PD-L1) level in tumor by immunohistochemistry (IHC) is widely used to predict response to check point inhibitor therapy. In particular, the Dako PD-L1 (22C3) antibody is a common companion diagnostic to the monoclonal antibody drug Keytruda® (pembrolizumab) in non-small cell lung cancer (NSCLC).1 However, for the practicing pathologist, interpretation of the PD-L1 (22C3) assay is cumbersome and time consuming. Manual pathologist scoring also suffers from poor intra- and inter-pathologist precision, particularly around the cut-off point.2 In this clinical validation study, we developed an image analysis (IA) based solution to accurately and precisely score digital images obtained from PD-L1 stained NSCLC tissues for making clinical enrollment decisions.Methods10 NSCLC tissue samples were purchased from a qualified vendor and IHC stained for PD-L1; 4 of these samples had serial sections stained on two separate days. Stained slides were scanned at 20X magnification and analyzed using Flagship Biosciences’ IA solutions that quantify PD-L1 expression and separate tumor and stromal compartments. Resulting image markups of cell detection and PD-L1 expression were reviewed by an MD pathologist for acceptance. PD-L1 staining was evaluated by digital IA in the sample’s tumor compartment for Total Proportion Score (TPS,%). Assay specificity was defined by ≥ 90% of the tissue cohort exhibiting appropriate cell recognition (≥ 90% cells correctly recognized as determined by the pathologist), with ≤ 10% false positive rate for staining classification. Sensitivity was defined by ≥ 90% of the cohort exhibiting appropriate cell identification (≥ 90% cells correctly identified), with ≤ 10% false negative rate for staining classification. Accuracy was defined by the combination of sensitivity and specificity and precision was defined by concordance of the binned TPS (<1%, ≥ 1%, ≥ 50%) in ≥ 80% of the samples stained on multiple days.ResultsThe preliminary results show that IA can yield high analytical sensitivity, specificity, accuracy, and precision in the determination of the PD-L1 score. 100% of the tissue cohort met criteria for analytical specificity, sensitivity, and accuracy and 100% of the samples stained on multiple days met the precision criteria.ConclusionsThis data demonstrates the feasibility of an IA approach as applied to PD-L1 (22C3) scoring. Ongoing experiments include application of the developed 22C3 algorithm on a separate cohort of 20 NSCLC samples to determine the correlation of digital scoring and scoring obtained by three pathologists. Additionally, we will evaluate the precision obtained by digital scoring in relation to the intra- and inter-pathologist concordance.ReferencesIncorvaia L, Fanale D, Badalamenti G, et al. Programmed death ligand 1 (PD-L1) as a predictive biomarker for pembrolizumab therapy in patients with advanced non-small-cell lung cancer (NSCLC). Adv Ther 2019;36:2600–2617.Rimm DL, Han G, Taube JM, et al. A prospective, multi-institutional, pathologist-based assessment of 4 immunohistochemistry assays for PD-L1 expression in non–small cell lung cancer. JAMA Oncol 2017;3:1051–1058.

Correlation of gefitinib and its metabolites with gefitinib induced rash in patients with non-small cell lung cancer (NSCLC).

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21712-e21712
Author(s):  
Shaoxing Guan ◽  
Xi Chen ◽  
Min Huang ◽  
Li Zhang ◽  
Xueding Wang
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Multivariate Logistic Regression Analysis ◽  
Small Cell ◽  
Therapeutic Drug ◽  
Small Cell Lung ◽  
Cutoff Values ◽  
Nsclc Patients ◽  
Severe Rash

e21712 Background: Gefitinib induced rash is the most common adverse reaction and severe rash of gefitinib often leads to discontinuation or termination of treatment. The concentrations of drug and its metabolites may affect drug induced toxicities, however, the association of gefitinib/metabolites with gefitinib-induced rash are poorly investigated. Therefore, we explored the association between concentrations of gefitinib and its four metabolites with gefitinib-induced rash in non-small cell lung cancer (NSCLC) patients. Methods: A total of 180 advanced NSCLC patients carrying EGFR sensitive mutations receiving gefitinib were enrolled. The concentrations of gefitinib, and its four metabolites including M537194, M387783, M523595 and M605211 were determined by liquid chromatography–tandem mass spectrometry (LC–MS/MS). The associations between concentration of gefitinib/its metabolites and gefitinib-induced rash were analyzed by Mann-Whiney U test. Operating characteristic curves(ROC) were used to determine gefitinib/metabolites cutoff values for gefitinib-induced rash. Results: M605211 was first detected in plasma in NSCLC patients. The concentrations of gefitinib and M605211, M537194 were found to be correlated with the incidence of gefitinib-induced rash ( P= 0.0002, 0.027 and 0.0097, respectively), moreover, the concentration of gefitinib was correlated with severe rash (Grade 0,1 vs. 2+, P= 0.017). Multivariate Logistic regression analysis showed that only gefitinib concentration was independent risk factor for gefitinib-induced rash (grade 0 vs grade1+, OR = 1.006, 95%CI (1.002-1.009), P = 0.00078; grade0,1 vs grade2+, OR = 1.003, 95%CI (1.001-1.005), P = 0.015, respectively). The cutoff values of gefitinib were 160.2 ng/ml (grade 0 vs grade1+, sensitivity = 78.7%, specificity = 47.7%, area under the curve (AUC) = 0.686, P = 0.0002, 95% CI (0.592-0.779)) and 201.7ng/ml (grade0,1 vs grade2+, sensitivity = 69.3%, specificity = 47.6%, AUC = 0.605, P = 0.0168, 95%CI (0.521-0.689)). Conclusions: This research demonstrated that the concentration of gefitinib and its metabolites were associated with gefitinib induced rash in NSCLC patients. Therapeutic drug monitoring of gefitinib concentration may have potential improvement for optimization of treatment with gefitinib. Clinical trial information: NCT01994057.

Detection of non‐small cell lung cancer cells based on microfluidic polarization microscopic image analysis

2018 ◽  
Vol 40 (8) ◽  
pp. 1202-1211 ◽  
Author(s):  
Yanjuan Wang ◽  
Junsheng Wang ◽  
Jie Meng ◽  
Gege Ding ◽  
Zhi Shi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Analysis ◽  
Small Cell Lung Cancer ◽  
Cancer Cells ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Lung Cancer Cells ◽  
Microscopic Image ◽  
Small Cell Lung ◽  
Microscopic Image Analysis
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