Ethnic diversity in treatment response for colorectal cancer: proof of concept for radiomics-driven enrichment trials.

BACKGROUND: Several barriers hamper recruitment of diverse patient populations in multicenter clinical trials which determine efficacy of new systemic cancer therapies. PURPOSE: We assessed if quantitative analysis of computed tomography (CT) scans of metastatic colorectal cancer (mCRC) patients using imaging features that predict overall survival (OS) can unravel the association between ethnicity and efficacy. METHODS: We retrospectively analyzed CT images from 1584 mCRC patients in two phase III trials evaluating FOLFOX ± panitumumab (n = 331, 350) and FOLFIRI ± aflibercept (n = 437, 466) collected from August 2006 to March 2013. Primary and secondary endpoints compared RECIST1.1 response at month-2 and delta tumor volume at month-2, respectively. An ancillary study compared imaging phenotype using a peer-reviewed radiomics-signature combining 3 imaging features to predict OS landmarked from month-2. Analysis was stratified by ethnicity. RESULTS: In total, 1584 patients were included (mean age, 60.25 ± 10.57 years; 969 men). Ethnicity was as follows: African (n = 50, 3.2%), Asian (n = 66, 4.2%), Caucasian (n = 1413, 89.2%), Latino (n = 27, 1.7%), Other (n = 28, 1.8%). Overall baseline tumor volume demonstrated Africans and Caucasians had more advanced disease (p < 0.001). Ethnicity was associated with treatment response. Response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. Overall delta tumor volume at month-2 demonstrated that Latino patients more likely experienced response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023). CONCLUSION: This study highlights how clinical trials that inadequately represent minority groups may impact associated translational work. In appropriately powered studies, radiomics features may allow us to unravel associations between ethnicity and treatment efficacy, better elucidate mechanisms of resistance, and promote diversity in trials through predictive enrichment. CLINICAL RELEVANCE STATEMENT: Radiomics could promote clinical trial diversity through predictive enrichment, hence benefit to historically underrepresented racial/ethnic groups that may respond variably to treatment due to socioeconomic factors and built environment, collectively referred to as social determinants of health. KEY POINTS: •Findings indicate ethnicity was associated with treatment response across all 3 endpoints. First, response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. •Second, the overall delta tumor volume at month-2 demonstrated that Latino patients were more likely to experience response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023).

Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes.

OBJECTIVES: To investigate the effect of CT image acquisition parameters on the performance of radiomics in classifying benign and malignant pulmonary nodules (PNs) with respect to nodule size. METHODS: We retrospectively collected CT images of 696 patients with PNs from March 2015 to March 2018. PNs were grouped by nodule diameter: T1a (diameter ≤ 1.0 cm), T1b (1.0 cm < diameter ≤ 2.0 cm), and T1c (2.0 cm < diameter ≤ 3.0 cm). CT images were divided into four settings according to slice-thickness-convolution-kernels: setting 1 (slice thickness/reconstruction type: 1.25 mm sharp), setting 2 (5 mm sharp), setting 3 (5 mm smooth), and random setting. We created twelve groups from two interacting conditions. Each PN was segmented and had 1160 radiomics features extracted. Non-redundant features with high predictive ability in training were selected to build a distinct model under each of the twelve subsets. RESULTS: The performance (AUCs) on predicting PN malignancy were as follows: T1a group: 0.84, 0.64, 0.68, and 0.68; T1b group: 0.68, 0.74, 0.76, and 0.70; T1c group: 0.66, 0.64, 0.63, and 0.70, for the setting 1, setting 2, setting 3, and random setting, respectively. In the T1a group, the AUC of radiomics model in setting 1 was statistically significantly higher than all others; In the T1b group, AUCs of radiomics models in setting 3 were statistically significantly higher than some; and in the T1c group, there were no statistically significant differences among models. CONCLUSIONS: For PNs less than 1 cm, CT image acquisition parameters have a significant influence on diagnostic performance of radiomics in predicting malignancy, and a model created using images reconstructed with thin section and a sharp kernel algorithm achieved the best performance. For PNs larger than 1 cm, CT reconstruction parameters did not affect diagnostic performance substantially. KEY POINTS: • CT image acquisition parameters have a significant influence on the diagnostic performance of radiomics in pulmonary nodules less than 1 cm. • In pulmonary nodules less than 1 cm, a radiomics model created by using images reconstructed with thin section and a sharp kernel algorithm achieved the best diagnostic performance. • For PNs larger than 1 cm, CT image acquisition parameters do not affect diagnostic performance substantially.

Comparing RECIST 1.1 and iRECIST in advanced melanoma patients treated with pembrolizumab in a phase II clinical trial.

OBJECTIVES: To compare tumor best overall response (BOR) by RECIST 1.1 and iRECIST, to explore the incidence of pseudoprogression in melanoma treated with pembrolizumab, and to assess the impact of pseudoprogression on overall survival (OS). METHODS: A total of 221 patients with locally advanced/unresectable melanoma who received pembrolizumab as part of KEYNOTE-002 trial were included in this study. Radiological assessment of imaging was centrally reviewed to assess tumor response. Incidence of discordance in BOR between RECIST 1.1 and iRECIST as well as rate of pseudoprogression were measured. OS of patients with pseudoprogression was compared with that of those with uncontrolled disease. RESULTS: Of the 221 patients in this cohort, 136 patients developed PD as per RECIST v1.1 and 78 patients with PD continued treatment and imaging beyond initial RECIST 1.1-defined PD. Among the 78 patients who continued therapy and imaging post-progression, RECIST 1.1 and iRECIST were discordant in 10 patients (12.8%) and pseudoprogression was encountered in 14 patients (17.9%). OS of patients with pseudoprogression was longer than that of patients with uncontrolled disease/true progression (29.9 months versus 8.0 months, p value < 0.001). CONCLUSIONS: Effectiveness of immunotherapy in clinical trials depends on the criterion used to assess tumor response (RECIST 1.1 vs iRECIST) with iRECIST being more appropriate to detect pseudoprogression and potentially prevent premature termination of effective therapy. Pseudoprogression was associated with improved OS in comparison with that of patients with uncontrolled disease. KEY POINTS: • Discordance between iRECIST and RECIST 1.1 was found in 12.8% of unresectable melanoma patients on pembrolizumab who continued therapy beyond initial RECIST 1.1-defined progression. • Pseudoprogression, captured with iRECIST, occurred in 17.9% and was significantly associated with improved overall survival in comparison with uncontrolled disease.

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.

Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules.

PURPOSE: To enhance clinician's decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans. MATERIAL AND METHODS: We retrospectively analyzed 178 cirrhotic patients from 27 institutions, with biopsy-proven liver nodules classified as indeterminate using the European Association for the Study of the Liver (EASL) guidelines. Patients were randomly assigned to a discovery cohort (142 patients (pts.)) and a validation cohort (36 pts.). Each liver nodule was segmented on each phase of triphasic CT scans, and 13,920 quantitative imaging features (12 sets of 1160 features each reflecting the phenotype at one single phase or its change between two phases) were extracted. Using machine-learning techniques, the signature was trained and calibrated (discovery cohort), and validated (validation cohort) to classify liver nodules as HCC vs. non-HCC. Effects of segmentation and contrast enhancement quality were also evaluated. RESULTS: Patients were predominantly male (88%) and CHILD A (65%). Biopsy was positive for HCC in 77% of patients. LI-RADS scores were not different between HCC and non-HCC patients. The signature included a single radiomics feature quantifying changes between arterial and portal venous phases: DeltaV-A_DWT1_LL_Variance-2D and reached area under the receiver operating characteristic curve (AUC) of 0.70 (95%CI 0.61-0.80) and 0.66 (95%CI 0.64-0.84) in discovery and validation cohorts, respectively. The signature was influenced neither by segmentation nor by contrast enhancement. CONCLUSION: A signature using a single feature was validated in a multicenter retrospective cohort to diagnose HCC in cirrhotic patients with indeterminate liver nodules. Artificial intelligence could enhance clinicians' decision by identifying a subgroup of patients with high HCC risk. KEY POINTS: • In cirrhotic patients with visually indeterminate liver nodules, expert visual assessment using current guidelines cannot accurately differentiate HCC from differential diagnoses. Current clinical protocols do not entail biopsy due to procedural risks. Radiomics can be used to non-invasively diagnose HCC in cirrhotic patients with indeterminate liver nodules, which could be leveraged to optimize patient management. • Radiomics features contributing the most to a better characterization of visually indeterminate liver nodules include changes in nodule phenotype between arterial and portal venous phases: the "washout" pattern appraised visually using EASL and EASL guidelines. • A clinical decision algorithm using radiomics could be applied to reduce the rate of cirrhotic patients requiring liver biopsy (EASL guidelines) or wait-and-see strategy (AASLD guidelines) and therefore improve their management and outcome.

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.

Differentiation of Focal-Type Autoimmune Pancreatitis From Pancreatic Ductal Adenocarcinoma Using Radiomics Based on Multiphasic Computed Tomography.

OBJECTIVES: The aim of this study was to develop a radiomics model for a differential diagnosis of focal-type autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma. METHODS: A total of 96 patients, 45 with AIP and 51 with pancreatic ductal adenocarcinoma, were retrospectively evaluated. All patients underwent pretreatment abdominal computed tomography imaging acquired at noncontrast, arterial, and venous phases. Furthermore, 1160 radiomics features were extracted from each phasic image to build radiomics models. The performance of radiomics model was evaluated by sensitivity, specificity, and accuracy. The results of radiomics model were also compared with those of radiologists' visual assessments. RESULTS: The sensitivity, specificity, and accuracy of the optimal radiomics model were 93.3%, 96.1%, and 94.8%, respectively. They were higher than those of the radiologists' assessments with sensitivity of 57.78% and 73.33%, specificity of 88.24% and 90.20%, and accuracy of 75.00% and 81.25%, respectively. CONCLUSION: Radiomics is helpful for a differential diagnosis of AIP in clinical practice as a noninvasive and quantitative method.

CT-Based Radiomics Model for Predicting Brain Metastasis in Category T1 Lung Adenocarcinoma.

OBJECTIVE. The purpose of this study is to develop and evaluate an unenhanced CT-based radiomics model to predict brain metastasis (BM) in patients with category T1 lung adenocarcinoma. MATERIALS AND METHODS. A total of 89 eligible patients with category T1 lung adenocarcinoma were enrolled and classified as patients with BM (n = 35) or patients without BM (n = 54). A total of 1160 quantitative radiomic features were extracted from unenhanced CT images of each patient. Three prediction models (the clinical model, the radiomics model, and a hybrid [clinical plus radiomics] model) were established. The ROC AUC value and 10-fold cross-validation were used to evaluate the prediction performance of the models. RESULTS. In terms of predictive performance, the mean AUC value was 0.759 (95% CI, 0.643-0.867; sensitivity, 82.9%; specificity, 57.4%) for the clinical model, 0.847 (95% CI, 0.739-0.915; sensitivity, 80.0%; specificity, 81.5%) for the radiomics model, and 0.871 (95% CI, 0.767-0.933; sensitivity = 82.9%, specificity = 83.3%) for the hybrid model. The hybrid and radiomics models (p = 0.0072 and 0.0492, respectively) performed significantly better than the clinical model. No significant difference was found between the radiomics model and the hybrid model (p = 0.1022). CONCLUSION. A CT-based radiomics model presented good predictive performance and great potential for predicting BM in patients with category T1 lung adenocarcinoma. As a promising adjuvant tool, it can be helpful for guiding BM screening and thus benefiting personalized surveillance for patients with lung cancer.

Application of Radiomics in Predicting the Malignancy of Pulmonary Nodules in Different Sizes.

OBJECTIVE. The purpose of this study was to investigate the utility of radiomics for predicting the malignancy of pulmonary nodules (PNs) of different sizes using unenhanced, thin-section CT images. MATERIALS AND METHODS. Patients with a single PN (n = 373) who underwent a preoperative chest CT were recruited retrospectively at Beijing Friendship Hospital from March 2015 to March 2018. Of the 373 PNs studied, 192 were benign and 181 were malignant. The lesions were classified into three groups (T1a, T1b, or T1c according to the 8th edition of the TNM staging system for lung cancer) on the basis of lesion diameters: T1a (diameter, 0-1 cm), T1b (1 cm < diameter ≤ 2 cm) and T1c (2 cm < diameter ≤ 3 cm). A total of 1160 radiomic features were extracted from PN segmentation on unenhanced CT images. We developed three radiomic models to predict PN malignancy in each group on the basis of the extracted radiomic features. Fivefold cross-validation was used to estimate AUC, accuracy, sensitivity, and specificity for indicating the performance of prediction models. RESULTS. The AUC, accuracy, sensitivity, and specificity for predicting PN malignancy in each group were 0.84, 0.77, 0.89, and 0.74 with the T1a model; 0.78, 0.73, 0.74, and 0.71 with the T1b model, and 0.79, 0.76, 0.77, and 0.73 with the T1c model, respectively. The most contributive radiomic features for predicting PN malignancy for groups T1a, T1b, and T1c were LoG_X_Uniformity, Intensity_Minimum, and Shape_SI9, respectively. CONCLUSION. Radiomic features based on unenhanced CT images can be used to predict the malignancy of pulmonary nodules. The radiomic T1a model showed superior prediction performance to the T1b and T1c models, and the best performance in terms of AUC and sensitivity was found for predicting the malignancy of T1a PN.

Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography.

OBJECTIVES: The aim of this study was to evaluate the performance of the radiomics method in classifying lung cancer histological subtypes based on multiphasic contrast-enhanced computed tomography (CT) images. METHODS: A total of 229 patients with pathologically confirmed lung cancer were retrospectively recruited. All recruited patients underwent nonenhanced and dual-phase chest contrast-enhanced CT; 1160 quantitative radiomics features were calculated to build a radiomics classification model. The performance of the classification models was evaluated by the receiver operating characteristic curve. RESULTS: The areas under the curve of radiomics models in classifying adenocarcinoma and squamous cell carcinoma, adenocarcinoma and small cell lung cancer, and squamous cell carcinoma and small cell lung cancer were 0.801, 0.857, and 0.657 (nonenhanced); 0.834, 0.855, and 0.619 (arterial phase); and 0.864, 0.864, and 0.664 (venous phase), respectively. Moreover, the application of contrast-enhanced CT may affect the selection of radiomics features. CONCLUSIONS: Our study indicates that radiomics may be a promising tool for noninvasive predicting histological subtypes of lung cancer based on the multiphasic contrast-enhanced CT images.

Nonenhancing Component of Clear Cell Renal Cell Carcinoma on Computed Tomography Correlates With Tumor Necrosis and Stage and Serves as a Size-Independent Prognostic Biomarker.

OBJECTIVES: This study aimed to quantify nonenhancing tumor (NT) component in clear cell renal cell carcinoma (ccRCC) and assess its association with histologically defined tumor necrosis, stage, and survival outcomes. METHODS: Among 183 patients with ccRCC, multi-institutional changes in computed tomography attenuation of tumor voxels were used to quantify percent of NT. Associations of NT with histologic tumor necrosis and tumor stage/grade were tested using Wilcoxon signed rank test and with survival outcomes using Kaplan-Meier curves/Cox regression analysis. RESULTS: Nonenhancing tumor was higher in ccRCC with tumor necrosis (11% vs 7%; P = 0.040) and higher pathological stage (P = 0.042 and P < 0.001, respectively). Patients with greater NT had higher incidence of cancer recurrence after resection (P < 0.001) and cancer-specific mortality (P < 0.001). CONCLUSION: Nonenhancing tumor on preoperative computed tomographic scans in patients with ccRCC correlates with tumor necrosis and stage and may serve as an independent imaging prognostic biomarker for cancer recurrence and cancer-specific survival.

Does breast MRI background parenchymal enhancement indicate metabolic activity? Qualitative and 3D quantitative computer imaging analysis.

PURPOSE: To investigate whether the degree of breast magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) is associated with the amount of breast metabolic activity measured by breast parenchymal uptake (BPU) of 18F-FDG on positron emission tomography / computed tomography (PET/CT). MATERIALS AND METHODS: An Institutional Review Board (IRB)-approved retrospective study was performed. Of 327 patients who underwent preoperative breast MRI from 1/1/12 to 12/31/15, 73 patients had 18F-FDG PET/CT evaluation performed within 1 week of breast MRI and no suspicious findings in the contralateral breast. MRI was performed on a 1.5T or 3.0T system. The imaging sequence included a triplane localizing sequence followed by sagittal fat-suppressed T2 -weighted sequence, and a bilateral sagittal T1 -weighted fat-suppressed fast spoiled gradient-echo sequence, which was performed before and three times after a rapid bolus injection (gadobenate dimeglumine, Multihance; Bracco Imaging; 0.1 mmol/kg) delivered through an IV catheter. The unaffected contralateral breast in these 73 patients underwent BPE and BPU assessments. For PET/CT BPU calculation, a 3D region of interest (ROI) was drawn around the glandular breast tissue and the maximum standardized uptake value (SUVmax ) was determined. Qualitative MRI BPE assessments were performed on a 4-point scale, in accordance with BI-RADS categories. Additional 3D quantitative MRI BPE analysis was performed using a previously published in-house technique. Spearman's correlation test and linear regression analysis was performed (SPSS, v. 24). RESULT: The median time interval between breast MRI and 18F-FDG PET/CT evaluation was 3 days (range, 0-6 days). BPU SUVmax mean value was 1.6 (SD, 0.53). Minimum and maximum BPU SUVmax values were 0.71 and 4.0. The BPU SUVmax values significantly correlated with both the qualitative and quantitative measurements of BPE, respectively (r(71) = 0.59, P < 0.001 and r(71) = 0.54, P < 0.001). Qualitatively assessed high BPE group (BI-RADS 3/4) had significantly higher BPU SUVmax of 1.9 (SD = 0.44) compared to low BPE group (BI-RADS 1/2) with an average BPU SUVmax of 1.17 (SD = 0.32) (P < 0.001). On linear regression analysis, BPU SUVmax significantly predicted qualitative and quantitative measurements of BPE (ß = 1.29, t(71) = 3.88, P < 0.001 and ß = 19.52, t(71) = 3.88, P < 0.001). CONCLUSION: There is a significant association between breast BPU and BPE, measured both qualitatively and quantitatively. Increased breast cancer risk in patients with high MRI BPE could be due to elevated basal metabolic activity of the normal breast tissue, which may provide a susceptible environment for tumor growth. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:753-759.

Visceral fat area, not body mass index, predicts postoperative 30-day morbidity in patients undergoing colon resection for cancer.

PURPOSE: Colectomy for cancer in obese patients is technically challenging and may be associated with worse outcomes. Whether visceral obesity, as measured on computed tomography, is a better predictor of complication than body mass index (BMI) or determines long-term oncologic outcomes has not been well characterized. This study examines the association between derived anthropometrics and postoperative complication and long-term oncologic outcomes. METHODS: Retrospective review of patients undergoing elective colectomy for cancer at a single tertiary-care center from 2010 to 2016. Adipose tissue distribution measurements, including visceral fat area (VFA), were determined from preoperative imaging. The primary outcome was 30-day postoperative complication; secondary outcomes included overall and disease-free survival. Multivariable logistic regression was performed to determine association between obesity metrics and outcome. RESULTS: Two hundred and sixty-four patients underwent 266 primary resections of colon cancer. Twenty-eight patients (10.5%) developed major morbidity (Clavien-Dindo grade ≥ III). VFA but not BMI was significantly associated with morbidity in multivariate analysis (p = 0.004, odds ratio 1.99, 95% confidence interval 1.25-3.19). No other imaging-derived anthropometric was associated with increased morbidity. In receiver operating characteristic analysis, VFA was predictive of major morbidity (area under curve 0.660). A cutoff value of VFA ≥ 191 cm2 was associated with 50% sensitivity and 76% specificity for predicting major morbidity. Patients with VFA ≥ 191cm2 had 19.4% risk of morbidity, whereas those with < 191 cm2 had 7.2% risk (relative risk ratio 2.69, unadjusted p = 0.004). Neither VFA nor BMI was associated with overall or disease-free survival. CONCLUSION: VFA but not BMI predicts morbidity following elective surgery for colon cancer.

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.

Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 µl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

Changes in liver and spleen volumes after living liver donation: a report from the Adult-to-Adult Living Donor Liver Transplantation Cohort Study (A2ALL).

Previous reports have drawn attention to persistently decreased platelet counts among liver donors. We hypothesized an etiologic association between altered platelet counts and postdonation splenomegaly and sought to explore this relationship. This study analyzed de-identified computed tomography/magnetic resonance scans of 388 donors from 9 Adult-to-Adult Living Donor Liver Transplantation Cohort Study centers read at a central computational image analysis laboratory. Resulting liver and spleen volumes were correlated with time-matched clinical laboratory values. Predonation liver volumes varied 2-fold in healthy subjects, even when they were normalized by the body surface area (BSA; range = 522-1887 cc/m(2) , n = 346). At month 3 (M3), postdonation liver volumes were, on average, 79% of predonation volumes [interquartile range (IQR) = 73%-86%, n = 165] and approached 88% at year 1 (Y1; IQR = 80%-93%, n = 75). The mean spleen volume before donation was 245 cc (n = 346). Spleen volumes greater than 100% of the predonation volume occurred in 92% of donors at M3 (n = 165) and in 88% at Y1 after donation (n = 75). We sought to develop a standard spleen volume (SSV) model to predict normal spleen volumes in donors before donation and found that decreased platelet counts, a younger age, a higher predonation liver volume, higher hemoglobin levels, and a higher BSA predicted a larger spleen volume (n = 344, R(2) = 0.52). When this was applied to postdonation values, some large volumes were underpredicted by the SSV model. Models developed on the basis of the reduced sample of postdonation volumes yielded smaller underpredictions. These findings confirm previous observations of thrombocytopenia being associated with splenomegaly after donation. The results of the SSV model suggest that the biology of this phenomenon is complex. This merits further long-term mechanistic studies of liver donors with an investigation of the role of other factors such as thrombopoietin and exposure to viral infections to better understand the evolution of the spleen volume after liver donation.

Brain injury visible on early MRI after subarachnoid hemorrhage might predict neurological impairment and functional outcome.

BACKGROUND: In subarachnoid hemorrhage (SAH), brain injury visible within 48 h of onset may impact on admission neurological disability and 3-month functional outcome. With volumetric MRI, we measured the volume of brain injury visible after SAH, and assessed the association with admission clinical grade and 3-month functional outcome. METHODS: Retrospective cohort study conducted in the Neurocritical Care Division, Columbia University Medical Center, New York, USA. On brain MRI acquired within 48 h of SAH-onset and before aneurysm-securing (n = 27), two blinded readers measured DWI and FLAIR-lesion volumes using semi-automated, computer segmentation software. RESULTS: Compared to post-resuscitation Hunt-Hess grade 1-3 (70 %), high-grade patients (30 %) had higher lesion volumes on DWI (34 ml [IQR: 0-64] vs. 2 ml [IQR: 0.5-7], P = 0.02) and on FLAIR (81 ml [IQR: 24-127] vs. 3 ml [IQR: 0-27], P = 0.02). On DWI, each 10 ml increase in lesion volume was associated with a 101 %-increase in the odds of presenting with 1 grade more in the Hunt-Hess scale (aOR 2.01, 95 % CI 1.10-3.68, P = 0.02), but was not significantly associated with 3-month outcome. On FLAIR, each 10 ml increase in lesion volume was associated with 34 % higher odds of a 1-point increase on the Hunt-Hess scale (aOR 1.34, 95 % CI 1.06-1.68, P = 0.01) and 139 % higher odds of a 1-point increase on the 3-month mRS (aOR 2.39, 95 % CI 1.13-5.07, P = 0.02). CONCLUSION: The volume of brain injury visible on DWI and FLAIR within 48 h after SAH is proportional to neurological impairment on admission. Moreover, FLAIR-imaging implicates chronic brain injury-predating SAH-as potentially relevant cause of poor functional outcome.

Test-retest reproducibility analysis of lung CT image features.

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.

Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

Epidermal growth factor receptor mutation in lung adenocarcinomas: relationship with CT characteristics and histologic subtypes.

PURPOSE: To retrospectively identify quantitative computed tomographic (CT) features that correlate with epidermal growth factor receptor (EGFR) mutation in surgically resected lung adenocarcinomas stratified by the International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS) classification in an East Asian cohort of patients known to have a high prevalence of EGFR mutations. MATERIALS AND METHODS: An institutional review board approved this study and waived informed consent. In 153 surgically resected lung adenocarcinomas, EGFR mutation was determined by direct DNA sequencing. Histologic subtype was classified according to IASLC/ATS/ERS classification of lung adenocarcinoma. At preoperative chest CT, the percentage of ground-glass opacity (GGO) volume and total tumor volume of each tumor were measured by using a semiautomated algorithm. Distribution of EGFR mutation according to histologic subtype, percentage of GGO volume, and total tumor volume was evaluated by using the Fisher exact test, the Student t test, trend analysis, and multiple logistic regression analysis. RESULTS: Exon 21 missense mutation was more frequent in lepidic predominant adenocarcinomas than in other histologic subtypes (odds ratio, 3.44; 95% confidence interval: 1.53, 7.74; P = .003). GGO volume percentage in tumors with exon 21 missense mutation (61.7% ± 31.9 [standard deviation]) was significantly higher than that in EGFR wild-type tumors (30.0% ± 38.5) (P = .0001) and exon 19-mutated tumors (28.9% ± 37.7) (P = .0006). A significant trend of prevalence of exon 21 missense mutation increasing along with increasing GGO volume (P = .0008) was found. CONCLUSION: GGO volume percentage in tumors with exon 21 missense mutation was significantly higher than that in tumors with other EGFR mutation status. This can be related to the fact that exon 21 missense mutation was significantly more frequent in lepidic predominant adenocarcinomas, including adenocarcinoma in situ, minimally invasive adenocarcinoma, and lepidic predominant invasive adenocarcinoma, according to IASLE/ATS/ERS classification.

Quantitative X-ray computed tomography peritoneography in malignant peritoneal mesothelioma patients receiving intraperitoneal chemotherapy.

BACKGROUND: Intraperitoneal chemotherapy is used to treat peritoneal surface-spreading malignancies. We sought to determine whether volume and surface area of the intraperitoneal chemotherapy compartments are associated with overall survival and posttreatment glomerular filtration rate (GFR) in malignant peritoneal mesothelioma (MPM) patients. METHODS: Thirty-eight MPM patients underwent X-ray computed tomography peritoneograms during outpatient intraperitoneal chemotherapy. We calculated volume and surface area of contrast-filled compartments by semiautomated computer algorithm. We tested whether these were associated with overall survival and posttreatment GFR. RESULTS: Decreased likelihood of mortality was associated with larger surface areas (p = 0.0201) and smaller contrast-filled compartment volumes (p = 0.0341), controlling for age, sex, histologic subtype, and presence of residual disease >0.5 cm postoperatively. Larger volumes were associated with higher posttreatment GFR, controlling for pretreatment GFR, body surface area, surface area, and the interaction between body surface area and volume (p = 0.0167). DISCUSSION: Computed tomography peritoneography is an appropriate modality to assess for maldistribution of intraperitoneal chemotherapy. In addition to identifying catheter failure and frank loculation, quantitative analysis of the contrast-filled compartment's surface area and volume may predict overall survival and cisplatin-induced nephrotoxicity. Prospective studies should be undertaken to confirm and extend these findings to other diseases, including advanced ovarian carcinoma.