Correction to: A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma.

The original version of this article, published on 21 February 2020, unfortunately contained a mistake.

A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma.

OBJECTIVES: Classification of histologic subgroups has significant prognostic value for lung adenocarcinoma patients who undergo surgical resection. However, clinical histopathology assessment is generally performed on only a small portion of the overall tumor from biopsy or surgery. Our objective is to identify a noninvasive quantitative imaging biomarker (QIB) for the classification of histologic subgroups in lung adenocarcinoma patients. METHODS: We retrospectively collected and reviewed 1313 CT scans of patients with resected lung adenocarcinomas from two geographically distant institutions who were seen between January 2014 and October 2017. Three study cohorts, the training, internal validation, and external validation cohorts, were created, within which lung adenocarcinomas were divided into two disease-free-survival (DFS)-associated histologic subgroups, the mid/poor and good DFS groups. A comprehensive machine learning- and deep learning-based analytical system was adopted to identify reproducible QIBs and help to understand QIBs' significance. RESULTS: Intensity-Skewness, a QIB quantifying tumor density distribution, was identified as the optimal biomarker for predicting histologic subgroups. Intensity-Skewness achieved high AUCs (95% CI) of 0.849(0.813,0.881), 0.820(0.781,0.856) and 0.863(0.827,0.895) on the training, internal validation, and external validation cohorts, respectively. A criterion of Intensity-Skewness ≤ 1.5, which indicated high tumor density, showed high specificity of 96% (sensitivity 46%) and 99% (sensitivity 53%) on predicting the mid/poor DFS group in the training and external validation cohorts, respectively. CONCLUSIONS: A QIB derived from routinely acquired CT was able to predict lung adenocarcinoma histologic subgroups, providing a noninvasive method that could potentially benefit personalized treatment decision-making for lung cancer patients. KEY POINTS: • A noninvasive imaging biomarker, Intensity-Skewness, which described the distortion of pixel-intensity distribution within lesions on CT images, was identified as a biomarker to predict disease-free-survival-associated histologic subgroups in lung adenocarcinoma. • An Intensity-Skewness of ≤ 1.5 has high specificity in predicting the mid/poor disease-free survival histologic patient group in both the training cohort and the external validation cohort. • The Intensity-Skewness is a feature that can be automatically computed with high reproducibility and robustness.

Baseline Radiomic Signature to Estimate Overall Survival in Patients With NSCLC.

INTRODUCTION: We aimed to define a baseline radiomic signature associated with overall survival (OS) using baseline computed tomography (CT) images obtained from patients with NSCLC treated with nivolumab or chemotherapy. METHODS: The radiomic signature was developed in patients with NSCLC treated with nivolumab in CheckMate-017, -026, and -063. Nivolumab-treated patients were pooled and randomized to training, calibration, or validation sets using a 2:1:1 ratio. From baseline CT images, volume of tumor lesions was semiautomatically segmented, and 38 radiomic variables depicting tumor phenotype were extracted. Association between the radiomic signature and OS was assessed in the nivolumab-treated (validation set) and chemotherapy-treated (test set) patients in these studies. RESULTS: A baseline radiomic signature was identified using CT images obtained from 758 patients. The radiomic signature used a combination of imaging variables (spatial correlation, tumor volume in the liver, and tumor volume in the mediastinal lymph nodes) to output a continuous value, ranging from 0 to 1 (from most to least favorable estimated OS). Given a threshold of 0.55, the sensitivity and specificity of the radiomic signature for predicting 3-month OS were 86% and 77.8%, respectively. The signature was identified in the training set of patients treated with nivolumab and was significantly associated (p < 0.0001) with OS in patients treated with nivolumab or chemotherapy. CONCLUSIONS: The radiomic signature provides an early readout of the anticipated OS in patients with NSCLC treated with nivolumab or chemotherapy. This could provide important prognostic information and may support risk stratification in clinical trials.

Radiomics Prediction of EGFR Status in Lung Cancer-Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data.

We investigated the performance of multiple radiomics feature extractors/software on predicting epidermal growth factor receptor mutation status in 228 patients with non-small cell lung cancer from publicly available data sets in The Cancer Imaging Archive. The imaging and clinical data were split into training (n = 105) and validation cohorts (n = 123). Two of the most cited open-source feature extractors, IBEX (1563 features) and Pyradiomics (1319 features), and our in-house software, Columbia Image Feature Extractor (CIFE) (1160 features), were used to extract radiomics features. Univariate and multivariate analyses were performed sequentially to predict EGFR mutation status using each individual feature extractor. Our univariate analysis integrated an unsupervised clustering method to identify nonredundant and informative candidate features for the creation of prediction models by multivariate analyses. In training, unsupervised clustering-based univariate analysis identified 5, 6, and 4 features from IBEX, Pyradiomics, and CIFE as candidate features, respectively. Multivariate prediction models using these features from IBEX, Pyradiomics, and CIFE yielded similar areas under the receiver operating characteristic curve of 0.68, 0.67, and 0.69. However, in validation, areas under the receiver operating characteristic curve of multivariate prediction models from IBEX, Pyradiomics, and CIFE decreased to 0.54, 0.56 and 0.64, respectively. Different feature extractors select different radiomics features, which leads to prediction models with varying performance. However, correlation between those selected features from different extractors may indicate these features measure similar imaging phenotypes associated with similar biological characteristics. Overall, attention should be paid to the generalizability of individual radiomics features and radiomics prediction models.

Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.

PURPOSE: Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib. EXPERIMENTAL DESIGN: Data were collected prospectively and analyzed retrospectively across multicenter clinical trials [nivolumab, n = 92, CheckMate017 (NCT01642004), CheckMate063 (NCT01721759); docetaxel, n = 50, CheckMate017; gefitinib, n = 46, (NCT00588445)]. Patients were randomized to training or validation cohorts using either a 4:1 ratio (nivolumab: 72T:20V) or a 2:1 ratio (docetaxel: 32T:18V; gefitinib: 31T:15V) to ensure an adequate sample size in the validation set. Radiomics signatures were derived from quantitative analysis of early tumor changes from baseline to first on-treatment assessment. For each patient, 1,160 radiomics features were extracted from the largest measurable lung lesion. Tumors were classified as treatment sensitive or insensitive; reference standard was median progression-free survival (NCT01642004, NCT01721759) or surgery (NCT00588445). Machine learning was implemented to select up to four features to develop a radiomics signature in the training datasets and applied to each patient in the validation datasets to classify treatment sensitivity. RESULTS: The radiomics signatures predicted treatment sensitivity in the validation dataset of each study group with AUC (95 confidence interval): nivolumab, 0.77 (0.55-1.00); docetaxel, 0.67 (0.37-0.96); and gefitinib, 0.82 (0.53-0.97). Using serial radiographic measurements, the magnitude of exponential increase in signature features deciphering tumor volume, invasion of tumor boundaries, or tumor spatial heterogeneity was associated with shorter overall survival. CONCLUSIONS: Radiomics signatures predicted tumor sensitivity to treatment in patients with NSCLC, offering an approach that could enhance clinical decision-making to continue systemic therapies and forecast overall survival.

Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer.

PURPOSE: To evaluate the variability of tumor unidimensional, bidimensional, and volumetric measurements on same-day repeat computed tomographic (CT) scans in patients with non-small cell lung cancer. MATERIALS AND METHODS: This HIPAA-compliant study was approved by the institutional review board, with informed patient consent. Thirty-two patients with non-small cell lung cancer, each of whom underwent two CT scans of the chest within 15 minutes by using the same imaging protocol, were included in this study. Three radiologists independently measured the two greatest diameters of each lesion on both scans and, during another session, measured the same tumors on the first scan. In a separate analysis, computer software was applied to assist in the calculation of the two greatest diameters and the volume of each lesion on both scans. Concordance correlation coefficients (CCCs) and Bland-Altman plots were used to assess the agreements between the measurements of the two repeat scans (reproducibility) and between the two repeat readings of the same scan (repeatability). RESULTS: The reproducibility and repeatability of the three radiologists' measurements were high (all CCCs, >or=0.96). The reproducibility of the computer-aided measurements was even higher (all CCCs, 1.00). The 95% limits of agreements for the computer-aided unidimensional, bidimensional, and volumetric measurements on two repeat scans were (-7.3%, 6.2%), (-17.6%, 19.8%), and (-12.1%, 13.4%), respectively. CONCLUSION: Chest CT scans are well reproducible. Changes in unidimensional lesion size of 8% or greater exceed the measurement variability of the computer method and can be considered significant when estimating the outcome of therapy in a patient.

Exploring intra- and inter-reader variability in uni-dimensional, bi-dimensional, and volumetric measurements of solid tumors on CT scans reconstructed at different slice intervals.

OBJECTIVE: Understanding magnitudes of variability when measuring tumor size may be valuable in improving detection of tumor change and thus evaluating tumor response to therapy in clinical trials and care. Our study explored intra- and inter-reader variability of tumor uni-dimensional (1D), bi-dimensional (2D), and volumetric (VOL) measurements using manual and computer-aided methods (CAM) on CT scans reconstructed at different slice intervals. MATERIALS AND METHODS: Raw CT data from 30 patients enrolled in oncology clinical trials was reconstructed at 5, 2.5, and 1.25 mm slice intervals. 118 lesions in the lungs, liver, and lymph nodes were analyzed. For each lesion, two independent radiologists manually and, separately, using computer software, measured the maximum diameter (1D), maximum perpendicular diameter, and volume (CAM only). One of them blindly repeated the measurements. Intra- and inter-reader variability for the manual method and CAM were analyzed using linear mixed-effects models and Bland-Altman method. RESULTS: For the three slice intervals, the maximum coefficients of variation for manual intra-/inter-reader variability were 6.9%/9.0% (1D) and 12.3%/18.0% (2D), and for CAM were 5.4%/9.3% (1D), 11.3%/18.8% (2D) and 9.3%/18.0% (VOL). Maximal 95% reference ranges for the percentage difference in intra-reader measurements for manual 1D and 2D, and CAM VOL were (-15.5%, 25.8%), (-27.1%, 51.6%), and (-22.3%, 33.6%), respectively. CONCLUSIONS: Variability in measuring the diameter and volume of solid tumors, manually and by CAM, is affected by CT slice interval. The 2.5mm slice interval provides the least measurement variability. Among the three techniques, 2D has the greatest measurement variability compared to 1D and 3D.

Assessing the effect of CT slice interval on unidimensional, bidimensional and volumetric measurements of solid tumours.

OBJECTIVES: To study the magnitude of differences in tumour unidimensional (1D), bidimensional (2D) and volumetric (VOL) measurements determined from computed tomography (CT) images reconstructed at 5, 2.5 and 1.25 mm slice intervals. MATERIALS AND METHODS: A total of 118 lesions in lung, liver and lymph nodes were selected from 30 patients enrolled in early phase clinical trials. Each CT scan was reconstructed at 5, 2.5 and 1.25 mm slice intervals during the image acquisition. Lesions were semi-automatically segmented on each interval image series and supervised by a radiologist. 1D, 2D and VOL were computed based on the final segmentation results. Average measurement differences across different slice intervals were obtained using linear mixed-effects analysis of variance models. RESULTS: Lesion diameters ranged from 6.1 to 80.1 mm (median 18.4 mm). The largest difference was seen between 1.25 and 5 mm (mean difference of 7.6% for 1D [P < 0.0001], 13.1% for 2D [P < 0.0001], -5.7% for VOL [P = 0.0001]). Mean differences between 1.25 and 2.5 mm were all within ±3.5% (within ±6% confidence interval). For VOL, there was a larger average difference between measurements on different slice intervals for the smaller lesions (<10 mm) compared with the larger lesions. CONCLUSIONS: Different slice intervals may give different 1D, 2D and VOL measurements. In clinical practice, it would be prudent to use the same slice interval for consecutive measurements.

Variability of lung tumor measurements on repeat computed tomography scans taken within 15 minutes.

PURPOSE: We use changes in tumor measurements to assess response and progression, both in routine care and as the primary objective of clinical trials. However, the variability of computed tomography (CT) -based tumor measurement has not been comprehensively evaluated. In this study, we assess the variability of lung tumor measurement using repeat CT scans performed within 15 minutes of each other and discuss the implications of this variability in a clinical context. PATIENTS AND METHODS: Patients with non-small-cell lung cancer and a target lung lesion ≥ 1 cm consented to undergo two CT scans within a period of minutes. Three experienced radiologists measured the diameter of the target lesion on the two scans in a side-by-side fashion, and differences were compared. RESULTS: Fifty-seven percent of changes exceeded 1 mm in magnitude, and 33% of changes exceeded 2 mm. Median increase and decrease in tumor measurements were +4.3% and -4.2%, respectively, and ranged from 23% shrinkage to 31% growth. Measurement changes were within ± 10% for 84% of measurements, whereas 3% met criteria for progression according to Response Evaluation Criteria in Solid Tumors (RECIST; ≥ 20% increase). Smaller lesions had greater variability of percent measurement change (P = .005). CONCLUSION: Apparent changes in tumor diameter exceeding 1 to 2 mm are common on immediate reimaging. Increases and decreases less than 10% can be a result of the inherent variability of reimaging. Caution should be exercised in interpreting the significance of small changes in lesion size in the care of individual patients and in the interpretation of clinical trial results.

A pilot study of volume measurement as a method of tumor response evaluation to aid biomarker development.

PURPOSE: Tissue biomarker discovery is potentially limited by conventional tumor measurement techniques, which have an uncertain ability to accurately distinguish sensitive and resistant tumors. Semiautomated volumetric measurement of computed tomography imaging has the potential to more accurately capture tumor growth dynamics, allowing for more exact separation of sensitive and resistant tumors and a more accurate comparison of tissue characteristics. EXPERIMENTAL DESIGN: Forty-eight patients with early stage non-small cell lung cancer and clinical characteristics of sensitivity to gefitinib were studied. High-resolution computed tomography was done at baseline and after 3 weeks of gefitinib. Tumors were then resected and molecularly profiled. Unidimensional and volumetric measurements were done using a semiautomated algorithm. Measurement changes were evaluated for their ability to differentiate tumors with and without sensitizing mutations. RESULTS: Forty-four percent of tumors had epidermal growth factor receptor-sensitizing mutations. Receiver operating characteristic curve analysis showed that volumetric measurement had a higher area under the curve than unidimensional measurement for identifying tumors harboring sensitizing mutations (P = 0.009). Tumor volume decrease of >24.9% was the imaging criteria best able to classify tumors with and without sensitizing mutations (sensitivity, 90%; specificity, 89%). CONCLUSIONS: Volumetric tumor measurement was better than unidimensional tumor measurement at distinguishing tumors based on presence or absence of a sensitizing mutation. Use of volume-based response assessment for the development of tissue biomarkers could reduce contamination between sensitive and resistant tumor populations, improving our ability to identify meaningful predictors of sensitivity.

Assessment of therapy responses and prediction of survival in malignant pleural mesothelioma through computer-aided volumetric measurement on computed tomography scans.

PURPOSE: The purposes of this study were (1) to calculate the tumor volume in patients with malignant pleural mesothelioma using computed tomography (CT) scan images and a computer-aided measurement technique and (2) to investigate whether the baseline volume, or volume change after chemotherapy, predicts patient survival. METHODS: We compiled the clinical characteristics and outcome from 30 patients enrolled in two clinical trials at our cancer center in which the patients were treated with induction chemotherapy followed by surgery and radiation. CT scans of 30 patients were obtained at baseline and after two cycles of chemotherapy. Tumor volumes were calculated using a semiautomated computer algorithm. Overall survival was measured using a landmark time at 3 months post-treatment start date such that all patients had already received two cycles of chemotherapy and a follow-up scan. Association of volume changes with overall survival were determined by a Cox Proportional Hazards Model or log-rank test. The relationship between both pre and postoperative clinical stage and baseline tumor volume was analyzed using the rank sum test. RESULTS: The median baseline tumor volume was 473 cm(3) (range, 61 cm(3)-2108 cm(3)). Patients with high preoperative stages (III and IV) had larger baseline tumor volume than those with low preoperative stages (I and II) (p = 0.05). Patients with baseline volumes smaller than 619 cm(3) tended to survive longer than those with baseline volumes larger than or equal to 619 cm(3) (p = 0.07). Percentage change of tumor volume from baseline to first follow-up CT after two cycles of chemotherapy was significantly associated with overall survival (hazard ratio: 1.94 [95% confidence interval, 1.05-3.60], p = 0.04). Whereas the relative change in modified RECIST measurements was not significantly associated with overall survival (hazard ratio: 1.06 [95% confidence interval, 0.96-1.16], p = 0.25). By classifying changes of tumor volumes between two scans into two groups, i.e., "increase" and "decrease," a significant difference in survival was found between those who increased and decreased after two cycles of chemotherapy (p = 0.03). CONCLUSIONS: Changes in tumor volume after two cycles of chemotherapy predicted overall survival in patients with malignant pleural mesothelioma. Tumor volume at baseline was shown to be associated with preoperative clinical stage and survival. Computer-aided volumetric measurements may enable more reliable therapeutic response assessment and could provide additional prognostic information.

Role of preoperative biliary drainage of liver remnant prior to extended liver resection for hilar cholangiocarcinoma.

BACKGROUND: In patients with hilar cholangiocarcinoma, ipsilateral en bloc hepatic resection improves survival but is associated with increased morbidity. Preoperative biliary drainage of the future liver remnant (FLR) and contralateral portal vein embolization (PVE) may improve perioperative outcome, but their routine use is controversial. This study analyses the impact of FLR volume and preoperative biliary drainage on postoperative hepatic insufficiency and mortality rates. METHODS: Patients who underwent hepatic resection and for whom adequate imaging data for FLR calculation were available were identified retrospectively. Patient demographic, operative and perioperative data were recorded and analysed. The volume of the FLR was calculated based on the total liver volume and the volume of the resection that was actually performed using semi-automated contouring of the liver on preoperative helical acquired scans. In patients subjected to preoperative biliary drainage, the preoperative imaging was reviewed to determine if the FLR had been decompressed. Hepatic insufficiency was defined as a postoperative rise in bilirubin of 5 mg/dl above the preoperative level that persisted for >5 days postoperatively. Operative mortality was defined as death related to the operation, whenever it occurred. RESULTS: Sixty patients were identified who underwent hepatic resection between 1997 and 2007 and for whom imaging data were available for analysis. During this period, preoperative biliary drainage of the FLR was used selectively and PVE was used in only one patient. The mean age of the patients was 64 +/- 11.6 years and 68% were male. The median length of stay was 14 days and the overall morbidity and mortality were 53% and 10%, respectively. Preoperative FLR volume was a predictor of hepatic insufficiency and death (P= 0.03). A total of 65% of patients had an FLR volume > or = 30% (39/60) of the total volume. No patient in this group had hepatic insufficiency, but there were two operative deaths (5%), both occurring in patients who underwent preoperative biliary drainage. By contrast, in the group with FLR < 30% (21/60, 35%), hepatic insufficiency was seen in five patients and operative mortality in four patients, and were strongly associated with lack of preoperative biliary drainage of the FLR (P = 0.009). Patients with an FLR > or = 30% were more likely to have radiographic evidence of ipsilateral lobar atrophy and hypertrophy of the FLR (46.2% vs. 9.5% in patients with FLR < 30%; P = 0.004). CONCLUSIONS: In patients undergoing liver resection for hilar cholangiocarcinoma, FLR volume of < 30% of total liver volume is associated with increased risk for hepatic insufficiency and death. Preoperative biliary drainage of the FLR appears to improve outcome if the predicted volume is < 30%. However, in patients with FLR > or = 30%, preoperative biliary drainage does not appear to improve perioperative outcome and, as many of these patients have hypertrophy of the FLR, PVE is likely to offer little benefit.