Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies.

Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.

Genetic Determinants of Outcome in Intrahepatic Cholangiocarcinoma.

BACKGROUND AND AIM: Genetic alterations in intrahepatic cholangiocarcinoma (iCCA) are increasingly well characterized, but their impact on outcome and prognosis remains unknown. APPROACH AND RESULTS: This bi-institutional study of patients with confirmed iCCA (n = 412) used targeted next-generation sequencing of primary tumors to define associations among genetic alterations, clinicopathological variables, and outcome. The most common oncogenic alterations were isocitrate dehydrogenase 1 (IDH1; 20%), AT-rich interactive domain-containing protein 1A (20%), tumor protein P53 (TP53; 17%), cyclin-dependent kinase inhibitor 2A (CDKN2A; 15%), breast cancer 1-associated protein 1 (15%), FGFR2 (15%), polybromo 1 (12%), and KRAS (10%). IDH1/2 mutations (mut) were mutually exclusive with FGFR2 fusions, but neither was associated with outcome. For all patients, TP53 (P < 0.0001), KRAS (P = 0.0001), and CDKN2A (P < 0.0001) alterations predicted worse overall survival (OS). These high-risk alterations were enriched in advanced disease but adversely impacted survival across all stages, even when controlling for known correlates of outcome (multifocal disease, lymph node involvement, bile duct type, periductal infiltration). In resected patients (n = 209), TP53mut (HR, 1.82; 95% CI, 1.08-3.06; P = 0.03) and CDKN2A deletions (del; HR, 3.40; 95% CI, 1.95-5.94; P < 0.001) independently predicted shorter OS, as did high-risk clinical variables (multifocal liver disease [P < 0.001]; regional lymph node metastases [P < 0.001]), whereas KRASmut (HR, 1.69; 95% CI, 0.97-2.93; P = 0.06) trended toward statistical significance. The presence of both or neither high-risk clinical or genetic factors represented outcome extremes (median OS, 18.3 vs. 74.2 months; P < 0.001), with high-risk genetic alterations alone (median OS, 38.6 months; 95% CI, 28.8-73.5) or high-risk clinical variables alone (median OS, 37.0 months; 95% CI, 27.6-not available) associated with intermediate outcome. TP53mut, KRASmut, and CDKN2Adel similarly predicted worse outcome in patients with unresectable iCCA. CDKN2Adel tumors with high-risk clinical features were notable for limited survival and no benefit of resection over chemotherapy. CONCLUSIONS: TP53, KRAS, and CDKN2A alterations were independent prognostic factors in iCCA when controlling for clinical and pathologic variables, disease stage, and treatment. Because genetic profiling can be integrated into pretreatment therapeutic decision-making, combining clinical variables with targeted tumor sequencing may identify patient subgroups with poor outcome irrespective of treatment strategy.

Recurrence After Resection of Pancreatic Cancer: Can Radiomics Predict Patients at Greatest Risk of Liver Metastasis?

BACKGROUND: Liver metastasis (LM) after pancreatic ductal adenocarcinoma (PDAC) resection is common but difficult to predict and has grave prognosis. We combined preoperative clinicopathological variables and quantitative analysis of computed tomography (CT) imaging to predict early LM. METHODS: We retrospectively evaluated patients with PDAC submitted to resection between 2005 and 2014 and identified clinicopathological variables associated with early LM. We performed liver radiomic analysis on preoperative contrast-enhanced CT scans and developed a logistic regression classifier to predict early LM (< 6 months). RESULTS: In 688 resected PDAC patients, there were 516 recurrences (75%). The cumulative incidence of LM at 5 years was 41%, and patients who developed LM first (n = 194) had the lowest 1-year overall survival (OS) (34%), compared with 322 patients who developed extrahepatic recurrence first (61%). Independent predictors of time to LM included poor tumor differentiation (hazard ratio (HR) = 2.30; P < 0.001), large tumor size (HR = 1.17 per 2-cm increase; P = 0.048), lymphovascular invasion (HR = 1.50; P = 0.015), and liver Fibrosis-4 score (HR = 0.89 per 1-unit increase; P = 0.029) on multivariate analysis. A model using radiomic variables that reflect hepatic parenchymal heterogeneity identified patients at risk for early LM with an area under the receiver operating characteristic curve (AUC) of 0.71; the performance of the model was improved by incorporating preoperative clinicopathological variables (tumor size and differentiation status; AUC = 0.74, negative predictive value (NPV) = 0.86). CONCLUSIONS: We confirm the adverse survival impact of early LM after resection of PDAC. We further show that a model using radiomic data from preoperative imaging combined with tumor-related variables has great potential for identifying patients at high risk for LM and may help guide treatment selection.

Differences in Liver Parenchyma are Measurable with CT Radiomics at Initial Colon Resection in Patients that Develop Hepatic Metastases from Stage II/III Colon Cancer.

BACKGROUND: Currently, there are no methods to identify patients with an increased risk of liver metastases to guide patient selection for liver-directed therapies. We tried to determine whether quantitative image features (radiomics) of the liver obtained from preoperative staging CT scans at the time of initial colon resection differ in patients that subsequently develop liver metastases, extrahepatic metastases, or demonstrate prolonged disease-free survival. METHODS: Patients who underwent resection of stage II/III colon cancer from 2004 to 2012 with available preoperative CT scans were included in this single-institution, retrospective case-control study. Patients were grouped by initial recurrence patterns: liver recurrence, extrahepatic recurrence, or no evidence of disease at 5 years. Radiomic features of the liver parenchyma extracted from CT images were compared across groups. RESULTS: The cohort consisted of 120 patients divided evenly between three recurrence groups, with an equal number of stage II and III patients in each group. After adjusting for multiple comparisons, 44 of 254 (17%) imaging features displayed different distributions across the three patient groups (p < 0.05), with the clearest distinction between those with liver recurrence and no evidence of disease. Increased heterogeneity in the liver parenchyma by radiomic analysis was protective of liver metastases. CONCLUSIONS: CT radiomics is a promising tool to identify patients at high risk of developing liver metastases and is worthy of further investigation and validation.

Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation.

OBJECTIVES: This study aims to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas (PDAC) in patients who underwent consecutive contrast-enhanced computed tomography (CECT) scans. METHODS: In this IRB-approved and HIPAA-compliant retrospective study, 37 pairs of scans from 37 unique patients who underwent CECTs within a 2-week interval were included in the analysis of the reproducibility of features derived from pancreatic parenchyma, and a subset of 18 pairs of scans were further analyzed for the reproducibility of features derived from PDAC. In each patient, pancreatic parenchyma and pancreatic tumor (when present) were manually segmented by two radiologists independently. A total of 266 radiomic features were extracted from the pancreatic parenchyma and tumor region and also the volume and diameter of the tumor. The concordance correlation coefficient (CCC) was calculated to assess feature reproducibility for each patient in three scenarios: (1) different radiologists, same CECT; (2) same radiologist, different CECTs; and (3) different radiologists, different CECTs. RESULTS: Among pancreatic parenchyma-derived features, using a threshold of CCC > 0.90, 58/266 (21.8%) and 48/266 (18.1%) features met the threshold for scenario 1, 14/266 (5.3%) and 15/266 (5.6%) for scenario 2, and 14/266 (5.3%) and 10/266 (3.8%) for scenario 3. Among pancreatic tumor-derived features, 11/268 (4.1%) and 17/268 (6.3%) features met the threshold for scenario 1, 1/268 (0.4%) and 5/268 (1.9%) features met the threshold for scenario 2, and no features for scenario 3 met the threshold, respectively. CONCLUSIONS: Variations between CECT scans affected radiomic feature reproducibility to a greater extent than variation in segmentation. A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions. KEY POINTS: • For pancreatic-derived radiomic features from contrast-enhanced CT (CECT), fewer than 25% are reproducible (with a threshold of CCC < 0.9) in a clinical heterogeneous dataset. • Variations between CECT scans affected the number of reproducible radiomic features to a greater extent than variations in radiologist segmentation. • A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions.

Regional differences in gallbladder cancer pathogenesis: Insights from a multi-institutional comparison of tumor mutations.

BACKGROUND: Although rare in the United States, gallbladder cancer (GBCA) is a common cause of cancer death in some parts of the world. To investigate regional differences in pathogenesis and outcomes for GBCA, tumor mutations were analyzed from a sampling of specimens. METHODS: Primary tumors from patients with GBCA who were treated in Chile, Japan, and the United States between 1999 and 2016 underwent targeted sequencing of known cancer-associated genes. Fisher exact and Kruskal-Wallis tests assessed differences in clinicopathologic and genetic factors. Kaplan-Meier methods evaluated differences in overall survival from the time of surgery between mutations. RESULTS: A total of 81 patients were included. Japanese patients (11 patients) were older (median age, 72 years [range, 54-81 years]) compared with patients from Chile (21 patients; median age, 59 years [range, 32-73 years]) and the United States (49 patients; median age, 66 years [range, 46-87 years]) (P = .002) and had more well-differentiated tumors (46% vs 0% for Chile/United States; P < .001) and fewer gallstone-associated cancers (36% vs 67% for Chile and 69% for the United States; P = .13). Japanese patients had a median mutation burden of 6 (range, 1-23) compared with Chile (median mutation burden, 7 [range, 3-20]) and the United States (median mutation burden, 4 [range, 0-27]) (P = .006). Tumors from Japanese patients lacked AT-rich interaction domain 1A (ARID1A) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) mutations, whereas Chilean tumors lacked Erb-B2 receptor tyrosine kinase 3 (ERBB3) and AT-rich interaction domain 2 (ARID2) mutations. SMAD family member 4 (SMAD4) was found to be mutated similarly across centers (38% in Chile, 36% in Japan, and 27% in the United States; P = .68) and was univariately associated with worse overall survival (median, 10 months vs 25 months; P = .039). At least one potentially actionable gene was found to be altered in 80% of tumors. CONCLUSIONS: Differences in clinicopathologic variables suggest the possibility of distinct GBCA pathogenesis in Japanese patients, which may be supported by differences in mutation pattern. Among all centers, SMAD4 mutations were detected in approximately one-third of patients and may represent a converging factor associated with worse survival. The majority of patients carried mutations in actionable gene targets, which may inform the design of future trials.

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

OBJECTIVES: This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM). METHODS: Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R2. Clinicopatholologic factors were assessed for correlation with response. RESULTS: 157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05). CONCLUSION: Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies. KEY POINTS: • Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be measured with quantitative imaging. • Prediction model constructed that predicts volumetric response with 20% error suggesting that quantitative imaging holds promise to better select patients for specific treatments.

Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis.

BACKGROUND: Intraductal papillary mucinous neoplasms (IPMNs) are radiographically identifiable potential precursor lesions of pancreatic adenocarcinoma. While resection is recommended when main duct dilation is present, management of branch duct IPMN (BD-IPMN) remains controversial. This study sought to evaluate whether preoperative quantitative imaging features of BD-IPMNs could distinguish low-risk disease (low- and intermediate-grade dysplasia) from high-risk disease (high-grade dysplasia and invasive carcinoma). METHODS: Patients who underwent resection between 2005 and 2015 with pathologically proven BD-IPMN and a preoperative CT scan were included in the study. Quantitative image features were extracted using texture analysis and a novel quantitative mural nodularity feature developed for the study. Significant features on univariate analysis were combined with clinical variables to build a multivariate prediction model. RESULTS: Within the study group of 103 patients, 76 (74%) had low-risk disease and 27 (26%) had high-risk disease. Quantitative imaging features were prognostic of low-vs. high-risk disease. The model based only on clinical variables achieved an AUC of 0.67 and 0.79 with the addition of quantitative imaging features. CONCLUSION: Quantitative image analysis of BD-IPMNs is a novel method that may enable risk stratification. External validation may provide a reliable non-invasive prognostic tool for clinicians.

Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis.

BACKGROUND: Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation. RESULTS: A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data. CONCLUSION: We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

3D image guidance assisted identification of colorectal cancer liver metastases not seen on intraoperative ultrasound: results from a prospective trial.

BACKGROUND: Neoadjuvant treatment of colorectal liver metastases has become increasingly common, and while effective, often renders small metastases difficult to visualize on intraoperative US. The objective of this study was to determine the utility of a 3D image-guidance system in patients with intraoperative sonographically-occult CRLM. METHODS: 50 patients with at least one CRLM ≤ 1.5 cm were enrolled in this prospective trial of an FDA-approved Explorer image-guidance system. If the tumor(s) seen on preoperative imaging were not identified with intraoperative US, Explorer was used to target the US examination to the involved area for a more focused assessment. The primary endpoint was the proportion of cases with sonographically-occult metastases identified using Explorer. RESULTS: Forty-eight patients with preoperative scans within eight weeks of surgery were included for analysis. Forty-six patients were treated with preoperative chemotherapy (median 4 months, range 2-24 months). Overall, 22 sonographically-occult tumors in 14 patients were interrogated by Explorer, of which 15 tumors in 10 patients were located with image-guidance assistance. The only difference between patients with tumors not identified on US and those who did was the number of tumors (median 3 vs. 2, p = 0.018). CONCLUSION: 3D image-guidance can assist in identifying small CRLM, particularly after treatment with chemotherapy. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02806037, https://clinicaltrials.gov/ct2/show/NCT02806037.

Computed Tomography Image Texture: A Noninvasive Prognostic Marker of Hepatic Recurrence After Hepatectomy for Metastatic Colorectal Cancer.

BACKGROUND: Recurrence after resection of colorectal liver metastases (CRLMs) occurs in up to 75% of patients. Preoperative prediction of hepatic recurrence may inform therapeutic strategies at the time of initial resection. Texture analysis (TA) is an established technique that quantifies pixel intensity variations (heterogeneity) on cross-sectional imaging. We hypothesized that tumoral and parenchymal changes that are predictive of overall survival (OS) and recurrence in the future liver remnant (FLR) can be detected using TA on preoperative computed tomography (CT) images. METHODS: Patients who underwent resection for CRLM between 2003 and 2007 with appropriate preoperative CT scans were included (n = 198) in this retrospective study. Texture features extracted from the tumor and FLR, and clinicopathologic variables, were incorporated into a multivariable survival model. RESULTS: Quantitative imaging features of the FLR were an independent predictor of both OS and hepatic disease-free survival (HDFS). Tumor texture showed significant association with OS. TA of the FLR allowed patient stratification into two groups, with significantly different risks of hepatic recurrence (hazard ratio 2.09, 95% confidence interval 1.33-3.28; p = 0.001). Patients with homogeneous parenchyma had approximately twice the risk of hepatic recurrence (41 vs. 20%). CONCLUSION: TA of the tumor and FLR are independently associated with OS, and TA of the FLR is independently associated with HDFS. Patients with homogeneous parenchyma had a significantly higher risk of hepatic recurrence. Preoperative TA of the liver represents a potential biomarker to identify patients at risk of liver recurrence after resection for CRLM.

Assessing splenic enlargement on CT by unidimensional measurement changes in patients with colorectal liver metastases.

PURPOSE: The aim of this study was to assess splenic volume and to correlate unidimensional measurements with reference volumetric changes in chemotherapy-treated patients with colorectal cancer (CRC) liver metastases. METHODS: Forty consecutive patients were selected from the cohort of a previously reported study of chemotherapy-related morbidity following major hepatectomy for CRC liver metastases. Patients were treated for 6 months prior to resection, with imaging performed at baseline and after 6 months of chemotherapy. Three unidimensional spleen measurements were recorded-width, thickness, and height (W, T, and H). Reference splenic volume was measured at baseline and after chemotherapy. The best unidimensional splenic measurement was determined by regression analysis. The 95% CI for the predicted values and R (2) values was calculated for each regression. The percentage of volume increase at 6 months was calculated. RESULTS: W and H showed the highest correlation with splenic volume prior to and following chemotherapy (R (2) = 0.65-0.74, p < 0.001), while T showed a low correlation (R (2) = 0.11 and 0.18, p < 0.05). The mean reference splenic volume increased after 6 months of chemotherapy compared to baseline (326 vs. 278 mL). Splenic volume changes showed the highest correlation with changes in W (R (2) = 0.56, p < 0.001), then H (R (2) = 0.40, p < 0.001), but were not significantly correlated with changes in T (R (2) = 0.01, p = 0.055). CONCLUSIONS: Our results show the potential utility of measuring changes in splenic width to predict clinically significant changes in splenic volume in chemotherapy-treated patients with CRC liver metastases.

Early trends in serum phosphate and creatinine levels are associated with mortality following major hepatectomy.

BACKGROUND: Mortality after major hepatectomy remains high and is frequently related to post-hepatectomy liver failure (PHLF). Other than pre-existing liver disease and a small future liver remnant, few patient factors or early postoperative indicators identify patients at elevated risk for PHLF and mortality. METHODS: Data on demographics, comorbidities, operative procedures and postoperative laboratory trends were reviewed for patients submitted to major hepatectomy (at least three Couinaud segments) for malignancy during 1998-2013. These factors were compared among patients who died within 90 days, survivors who met the 50-50 criteria and all remaining survivors. RESULTS: A total of 1528 patients underwent major hepatectomy during the study period. Of these, 947 had metastatic colorectal cancer and underwent resection of a median of four segments. Overall, 49 patients (3.2%) died within 90 days of surgery and 48 patients (3.1%) met the 50-50 criteria for PHLF; 30 of these patients survived 90 days. Operative blood loss was higher in patients who died within 90 days compared with survivors (1.0 l versus 0.5 l; P < 0.001). Despite equivalent perioperative resuscitation and urine output, non-survivors had higher creatinine and phosphate levels than survivors on postoperative day (PoD) 1 (1.1 mg/dl versus 0.9 mg/dl and 4.6 mg/dl versus 3.7 mg/dl, respectively; P < 0.001). CONCLUSIONS: Early trends in creatinine and phosphate (between the day of surgery and PoD 1) identify patients at risk for PHLF and mortality.

Medical image synthesis via conditional GANs: Application to segmenting brain tumours.

Accurate brain tumour segmentation is critical for tasks such as surgical planning, diagnosis, and analysis, with magnetic resonance imaging (MRI) being the preferred modality due to its excellent visualisation of brain tissues. However, the wide intensity range of voxel values in MR scans often results in significant overlap between the density distributions of different tumour tissues, leading to reduced contrast and segmentation accuracy. This paper introduces a novel framework based on conditional generative adversarial networks (cGANs) aimed at enhancing the contrast of tumour subregions for both voxel-wise and region-wise segmentation approaches. We present two models: Enhancement and Segmentation GAN (ESGAN), which combines classifier loss with adversarial loss to predict central labels of input patches, and Enhancement GAN (EnhGAN), which generates high-contrast synthetic images with reduced inter-class overlap. These synthetic images are then fused with corresponding modalities to emphasise meaningful tissues while suppressing weaker ones. We also introduce a novel generator that adaptively calibrates voxel values within input patches, leveraging fully convolutional networks. Both models employ a multi-scale Markovian network as a GAN discriminator to capture local patch statistics and estimate the distribution of MR images in complex contexts. Experimental results on publicly available MR brain tumour datasets demonstrate the competitive accuracy of our models compared to current brain tumour segmentation techniques.

Use of Response Permutation to Measure an Imaging Dataset's Susceptibility to Overfitting by Selected Standard Analysis Pipelines.

RATIONALE AND OBJECTIVES: This study demonstrates a method for quantifying the impact of overfitting on the receiving operator characteristic curve (AUC) when using standard analysis pipelines to develop imaging biomarkers. We illustrate the approach using two publicly available repositories of radiology and pathology images for breast cancer diagnosis. MATERIALS AND METHODS: For each dataset, we permuted the outcome (cancer diagnosis) values to eliminate any true association between imaging features and outcome. Seven types of classification models (logistic regression, linear discriminant analysis, Naïve Bayes, linear support vector machines, nonlinear support vector machine, random forest, and multi-layer perceptron) were fitted to each scrambled dataset and evaluated by each of four techniques (all data, hold-out, 10-fold cross-validation, and bootstrapping). After repeating this process for a total of 50 outcome permutations, we averaged the resulting AUCs. Any increase over a null AUC of 0.5 can be attributed to overfitting. RESULTS: Applying this approach and varying sample size and the number of imaging features, we found that failing to control for overfitting could result in near-perfect prediction (AUC near 1.0). Cross-validation offered greater protection against overfitting than the other evaluation techniques, and for most classification algorithms a sample size of at least 200 was required to assess as few as 10 features with less than 0.05 AUC inflation attributable to overfitting. CONCLUSION: This approach could be applied to any curated dataset to suggest the number of features and analysis approaches to limit overfitting.

Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases.

The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM. It has been hypothesized that evidence for recurrence could be found via quantitative image analysis on preoperative CT imaging of the future liver remnant before resection. To investigate this hypothesis, we have collected preoperative hepatic CT scans, clinicopathologic data, and recurrence/survival data, from a large, single-institution series of patients (n = 197) who underwent hepatic resection of CRLM. For each patient, we also created segmentations of the liver, vessels, tumors, and future liver remnant. The largest of its kind, this dataset is a resource that may aid in the development of quantitative imaging biomarkers and machine learning models for the prediction of post-resection hepatic recurrence of CRLM.

Mechanisms of Exercise Intolerance Across the Breast Cancer Continuum: A Pooled Analysis of Individual Patient Data.

PURPOSE: The purpose of this study is to evaluate the prevalence of abnormal cardiopulmonary responses to exercise and pathophysiological mechanism(s) underpinning exercise intolerance across the continuum of breast cancer (BC) care from diagnosis to metastatic disease. METHODS: Individual participant data from four randomized trials spanning the BC continuum ([1] prechemotherapy [n = 146], [2] immediately postchemotherapy [n = 48], [3] survivorship [n = 138], and [4] metastatic [n = 47]) were pooled and compared with women at high-risk of BC (BC risk; n = 64). Identical treadmill-based peak cardiopulmonary exercise testing protocols evaluated exercise intolerance (peak oxygen consumption; V̇O2peak) and other resting, submaximal, and peak cardiopulmonary responses. The prevalence of 12 abnormal exercise responses was evaluated. Graphical plots of exercise responses were used to identify oxygen delivery and/or uptake mechanisms contributing to exercise intolerance. Unsupervised, hierarchical cluster analysis was conducted to explore exercise response phenogroups. RESULTS: Mean V̇O2peak was 2.78 ml O2.kg-1·min-1 (95% confidence interval [CI], -3.94, -1.62 mL O2.kg-1·min-1; P < 0.001) lower in the pooled BC cohort (52 ± 11 yr) than BC risk (55 ± 10 yr). Compared with BC risk, the pooled BC cohort had a 2.5-fold increased risk of any abnormal cardiopulmonary response (odds ratio, 2.5; 95% confidence interval, 1.2, 5.3; P = 0.014). Distinct exercise responses in BC reflected impaired oxygen delivery and uptake relative to control, although considerable inter-individual heterogeneity within cohorts was observed. In unsupervised, hierarchical cluster analysis, six phenogroups were identified with marked differences in cardiopulmonary response patterns and unique clinical characteristics. CONCLUSIONS: Abnormal cardiopulmonary response to exercise is common in BC and is related to impairments in oxygen delivery and uptake. The identification of exercise response phenogroups could help improve cardiovascular risk stratification and guide investigation of targeted exercise interventions.

Multiplexed Imaging Mass Cytometry Analysis Characterizes the Vascular Niche in Pancreatic Cancer.

Oncogenesis and progression of pancreatic ductal adenocarcinoma (PDAC) are driven by complex interactions between the neoplastic component and the tumor microenvironment, which includes immune, stromal, and parenchymal cells. In particular, most PDACs are characterized by a hypovascular and hypoxic environment that alters tumor cell behavior and limits the efficacy of chemotherapy and immunotherapy. Characterization of the spatial features of the vascular niche could advance our understanding of inter- and intratumoral heterogeneity in PDAC. In this study, we investigated the vascular microenvironment of PDAC by applying imaging mass cytometry using a 26-antibody panel on 35 regions of interest across 9 patients, capturing more than 140,000 single cells. The approach distinguished major cell types, including multiple populations of lymphoid and myeloid cells, endocrine cells, ductal cells, stromal cells, and endothelial cells. Evaluation of cellular neighborhoods identified 10 distinct spatial domains, including multiple immune and tumor-enriched environments as well as the vascular niche. Focused analysis revealed differential interactions between immune populations and the vasculature and identified distinct spatial domains wherein tumor cell proliferation occurs. Importantly, the vascular niche was closely associated with a population of CD44-expressing macrophages enriched for a proangiogenic gene signature. Taken together, this study provides insights into the spatial heterogeneity of PDAC and suggests a role for CD44-expressing macrophages in shaping the vascular niche. Significance: Imaging mass cytometry revealed that pancreatic ductal cancers are composed of 10 distinct cellular neighborhoods, including a vascular niche enriched for macrophages expressing high levels of CD44 and a proangiogenic gene signature.

Applying Natural Language Processing to Single-Report Prediction of Metastatic Disease Response Using the OR-RADS Lexicon.

Generating Real World Evidence (RWE) on disease responses from radiological reports is important for understanding cancer treatment effectiveness and developing personalized treatment. A lack of standardization in reporting among radiologists impacts the feasibility of large-scale interpretation of disease response. This study examines the utility of applying natural language processing (NLP) to the large-scale interpretation of disease responses using a standardized oncologic response lexicon (OR-RADS) to facilitate RWE collection. Radiologists annotated 3503 retrospectively collected clinical impressions from radiological reports across several cancer types with one of seven OR-RADS categories. A Bidirectional Encoder Representations from Transformers (BERT) model was trained on this dataset with an 80-20% train/test split to perform multiclass and single-class classification tasks using the OR-RADS. Radiologists also performed the classification to compare human and model performance. The model achieved accuracies from 95 to 99% across all classification tasks, performing better in single-class tasks compared to the multiclass task and producing minimal misclassifications, which pertained mostly to overpredicting the equivocal and mixed OR-RADS labels. Human accuracy ranged from 74 to 93% across all classification tasks, performing better on single-class tasks. This study demonstrates the feasibility of the BERT NLP model in predicting disease response in cancer patients, exceeding human performance, and encourages the use of the standardized OR-RADS lexicon to improve large-scale prediction accuracy.

Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach.

The liver is a frequent site of benign and malignant, primary and metastatic tumors. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common primary liver cancers, and colorectal liver metastasis (CRLM) is the most common secondary liver cancer. Although the imaging characteristic of these tumors is central to optimal clinical management, it relies on imaging features that are often non-specific, overlap, and are subject to inter-observer variability. Thus, in this study, we aimed to categorize liver tumors automatically from CT scans using a deep learning approach that objectively extracts discriminating features not visible to the naked eye. Specifically, we used a modified Inception v3 network-based classification model to classify HCC, ICC, CRLM, and benign tumors from pretreatment portal venous phase computed tomography (CT) scans. Using a multi-institutional dataset of 814 patients, this method achieved an overall accuracy rate of 96%, with sensitivity rates of 96%, 94%, 99%, and 86% for HCC, ICC, CRLM, and benign tumors, respectively, using an independent dataset. These results demonstrate the feasibility of the proposed computer-assisted system as a novel non-invasive diagnostic tool to classify the most common liver tumors objectively.