Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer.

PURPOSE: High tumor production of the EGFR ligands, amphiregulin (AREG) and epiregulin (EREG), predicted benefit from anti-EGFR therapy for metastatic colorectal cancer (mCRC) in a retrospective analysis of clinical trial data. Here, AREG/EREG IHC was analyzed in a cohort of patients who received anti-EGFR therapy as part of routine care, including key clinical contexts not investigated in the previous analysis. EXPERIMENTAL DESIGN: Patients who received panitumumab or cetuximab ± chemotherapy for treatment of RAS wild-type mCRC at eight UK cancer centers were eligible. Archival formalin-fixed paraffin-embedded tumor tissue was analyzed for AREG and EREG IHC in six regional laboratories using previously developed artificial intelligence technologies. Primary endpoints were progression-free survival (PFS) and overall survival (OS). RESULTS: A total of 494 of 541 patients (91.3%) had adequate tissue for analysis. A total of 45 were excluded after central extended RAS testing, leaving 449 patients in the primary analysis population. After adjustment for additional prognostic factors, high AREG/EREG expression (n = 360; 80.2%) was associated with significantly prolonged PFS [median: 8.5 vs. 4.4 months; HR, 0.73; 95% confidence interval (CI), 0.56-0.95; P = 0.02] and OS [median: 16.4 vs. 8.9 months; HR, 0.66 95% CI, 0.50-0.86; P = 0.002]. The significant OS benefit was maintained among patients with right primary tumor location (PTL), those receiving cetuximab or panitumumab, those with an oxaliplatin- or irinotecan-based chemotherapy backbone, and those with tumor tissue obtained by biopsy or surgical resection. CONCLUSIONS: High tumor AREG/EREG expression was associated with superior survival outcomes from anti-EGFR therapy in mCRC, including in right PTL disease. AREG/EREG IHC assessment could aid therapeutic decisions in routine practice. See related commentary by Randon and Pietrantonio, p. 4021.

Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis: An Assessment.

RATIONALE AND OBJECTIVES: The purpose of this study is to improve accuracy of near-term breast cancer risk prediction by applying a new mammographic image conversion method combined with a two-stage artificial neural network (ANN)-based classification scheme. MATERIALS AND METHODS: The dataset included 168 negative mammography screening cases. In developing and testing our new risk model, we first converted the original grayscale value (GV)-based mammographic images into optical density (OD)-based images. For each case, our computer-aided scheme then computed two types of image features representing bilateral asymmetry and the maximum of the image features computed from GV and OD images, respectively. A two-stage classification scheme consisting of three ANNs was developed. The first stage included two ANNs trained using features computed separately from GV and OD images of 138 cases. The second stage included another ANN to fuse the prediction scores produced by two ANNs in the first stage. The risk prediction performance was tested using the rest 30 cases. RESULTS: With the two-stage classification scheme, the computed area under the receiver operating characteristic curve (AUC) was 0.816 ± 0.071, which was significantly higher than the AUC values of 0.669 ± 0.099 and 0.646 ± 0.099 achieved using two ANNs trained using GV features and OD features, respectively (P < .05). CONCLUSION: This study demonstrated that applying an OD image conversion method can acquire new complimentary information to those acquired from the original images. As a result, fusion image features computed from these two types of images yielded significantly higher performance in near-term breast cancer risk prediction.

Systemic response of coated-platelet and peripheral blood inflammatory cell indices after aneurysmal subarachnoid hemorrhage and long-term clinical outcome.

PURPOSE: Post-hemorrhage period after aneurysmal subarachnoid hemorrhage (aSAH) has several systemic manifestations including prothrombotic and pro-inflammatory states. Inter-relationship between these states using established/routine laboratory biomarkers and its long-term effect on clinical outcome is not well-defined. MATERIALS AND METHODS: Retrospective analysis of prospective cohort of 44 aSAH patients. Trend of procoagulant biomarkers [coated-platelets, mean platelet volume to platelet count (MPV:PLT)] and peripheral inflammatory biomarkers [platelet-lymphocyte ratio (PLR), neutrophil-platelet ratio (NLR)] were analyzed using regression analysis. Occurrence of delayed cerebral ischemia (DCI), modified Rankin score (mRS) of 3-6 and Montreal cognitive assessment (MoCA) of <26 at 1-year defined adverse clinical outcome. RESULTS: Patients with worse mRS and MoCA score had higher rise in coated-platelet compared to those with better scores [20.4 (IQR: 15.6, 32.9) vs. 10.95 (IQR: 6.1, 18.9), p = 0.003] and [16.9 (IQR: 13.4, 28.1) vs. 10.95 (IQR: 6.35, 18.65), p = 0.02] respectively. NLR and PLR trends showed significant initial decline followed by a gradual rise in NLR among those without DCI as compared to persistent low levels in those developing DCI (0.13 units/day vs. -0.07 units/day, p = 0.06). CONCLUSIONS: Coated-platelet rise after aSAH is associated with adverse long-term clinical outcome. NLR and PLR trends show an early immune-depressed state after aSAH.

Developing global image feature analysis models to predict cancer risk and prognosis.

In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images, which include steps in detecting and segmenting suspicious regions or tumors, followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors. However, due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors, segmenting subtle regions is often difficult and unreliable. Additionally, ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches. In our recent studies, we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis. We trained and tested several models using images obtained from full-field digital mammography, magnetic resonance imaging, and computed tomography of breast, lung, and ovarian cancers. Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice. Furthermore, the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis. Therefore, the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Radiological Estimation of Intracranial Blood Volume and Occurrence of Hydrocephalus Determines Stress-Induced Hyperglycemia After Aneurysmal Subarachnoid Hemorrhage.

Acute phase after aneurysmal subarachnoid hemorrhage (aSAH) is associated with several metabolic derangements including stress-induced hyperglycemia (SIH). The present study is designed to identify objective radiological determinants for SIH to better understand its contributory role in clinical outcomes after aSAH. A computer-aided detection tool was used to segment admission computed tomography (CT) images of aSAH patients to estimate intracranial blood and cerebrospinal fluid volumes. Modified Graeb score (mGS) was used as a semi-quantitative measure to estimate degree of hydrocephalus. The relationship between glycemic gap (GG) determined SIH, mGS, and estimated intracranial blood and cerebrospinal fluid volumes were evaluated using linear regression. Ninety-four [94/187 (50.3%)] among the study cohort had SIH (defined as GG > 26.7 mg/dl). Patients with SIH had 14.3 ml/1000 ml more intracranial blood volume as compared to those without SIH [39.6 ml (95% confidence interval, CI, 33.6 to 45.5) vs. 25.3 ml (95% CI 20.6 to 29.9), p = 0.0002]. Linear regression analysis of mGS with GG showed each unit increase in mGS resulted in 1.2 mg/dl increase in GG [p = 0.002]. Patients with SIH had higher mGS [median 4.0, interquartile range, IQR 2.0-7.0] as compared to those without SIH [median 2.0, IQR 0.0-6.0], p = 0.002. Patients with third ventricular blood on admission CT scan were more likely to develop SIH [67/118 (56.8%) vs. 27/69 (39.1%), p = 0.023]. Hence, the present study, using unbiased SIH definition and objective CT scan parameters, reports "dose-dependent" radiological features resulting in SIH. Such findings allude to a brain injury-stress response-neuroendocrine axis in etiopathogenesis of SIH.

Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms.

Contrast-enhanced digital mammography (CEDM) is a promising imaging modality in breast cancer diagnosis. This study aims to investigate how to optimally develop a computer-aided diagnosis (CAD) scheme of CEDM images to classify breast masses. A CEDM dataset of 111 patients was assembled, which includes 33 benign and 78 malignant cases. Each CEDM includes two types of images namely, low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting on LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron based machine learning classifiers that integrate with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. When applying CAD to DES and LE images with original segmentation, areas under ROC curves (AUC) were 0.759 ± 0.053 and 0.753 ± 0.047, respectively. After mapping the mass regions optimally segmented on DES images to LE images, AUC significantly increased to 0.848 ± 0.038 (p < 0.01). Study demonstrated that DES images eliminated overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. The study demonstrated that applying a novel approach to optimally map mass region segmented from DES images to LE images enabled CAD to yield significantly improved performance.

Applying a new bilateral mammographic density segmentation method to improve accuracy of breast cancer risk prediction.

PURPOSE: How to optimally detect bilateral mammographic asymmetry and improve risk prediction accuracy remains a difficult and unsolved issue. Our aim was to find an effective mammographic density segmentation method to improve accuracy of breast cancer risk prediction. METHODS: A dataset including 168 negative mammography screening cases was used. We applied a mutual threshold to bilateral mammograms of left and right breasts to segment the dense breast regions. The mutual threshold was determined by the median grayscale value of all pixels in both left and right breast regions. For each case, we then computed three types of image features representing asymmetry, mean and the maximum of the image features, respectively. A two-stage classification scheme was developed to fuse the three types of features. The risk prediction performance was tested using a leave-one-case-out cross-validation method. RESULTS: By using the new density segmentation method, the computed area under the receiver operating characteristic curve was 0.830 ± 0.033 and overall prediction accuracy was 81.0%, significantly higher than those of 0.633 ± 0.043 and 57.1% achieved by using the previous density segmentation method ([Formula: see text], t-test). CONCLUSIONS: A new mammographic density segmentation method based on a bilateral mutual threshold can be used to more effectively detect bilateral mammographic density asymmetry and help significantly improve accuracy of near-term breast cancer risk prediction.

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions.

The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a 'scoring fusion' artificial neural network classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793 ± 0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions.

Computer-aided classification of mammographic masses using visually sensitive image features.

PURPOSE: To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. METHODS: An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. RESULTS: Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. CONCLUSION: This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a "visual aid" interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features.

Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.

PURPOSE: To develop a new quantitative global kinetic breast magnetic resonance imaging (MRI) features analysis scheme and assess its feasibility to assess tumor response to neoadjuvant chemotherapy. MATERIALS AND METHODS: A dataset involving breast MR images acquired from 151 cancer patients before neoadjuvant chemotherapy was used. Among them, 63 patients had complete response (CR) and 88 had partial response (PR) to chemotherapy based on the RECIST criterion. A computer-aided detection (CAD) scheme was applied to segment breast region depicted on the breast MR images and computed a total of 10 kinetic image features to represent parenchyma enhancement either from the entire two breasts or the bilateral asymmetry between the two breasts. To classify between CR and PR cases, we tested an attribution selected classifier that integrates with an artificial neural network and a Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-case-out (LOCO)-based cross-validation method. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance. RESULTS: From the pool of initial 10 features, four features were selected by more than 90% times in the LOCO cross-validation iterations. Among them, three represent the bilateral asymmetry of kinetic features between two breasts. Using the classifier yielded AUC = 0.83 ± 0.04, which is significantly higher than using each individual feature to classify between CR and PR cases (P < 0.05). CONCLUSION: This study demonstrated that quantitative analysis of global kinetic features computed from breast MRI-acquired prechemotherapy has potential to generate a useful clinical marker that is associated with tumor response to neoadjuvant chemotherapy. J. Magn. Reson. Imaging 2016;44:1099-1106.

Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy.

PURPOSE: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. METHODS: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. RESULTS: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC=0.85±0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96±0.03 (p<0.01). CONCLUSIONS: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.