Contrast-Enhanced CT Density Predicts Response to Sunitinib Therapy in Metastatic Renal Cell Carcinoma Patients

Prognostic Utility of Parameters Derived From Pretreatment Dual-Layer Spectral-Detector CT in Patients With Metastatic Renal Cell Carcinoma

Background: New therapies have emerged for metastatic renal cell carcinoma (mRCC), though corresponding imaging markers are lacking. Dual-layer spectral-detector CT (DLCT) can quantify iodine concentration (IC) and effective atomic number (Zeffective), providing information beyond attenuation that may indicate mRCC prognosis. Objective: To assess the utility of the DLCT-derived parameters IC and Zeffective for predicting mRCC treatment response and survival. Methods: This prospective study (ClinicalTrials.gov identifier: NCT03616951) enrolled 120 participants with mRCC from January 2018 to January 2020 who underwent DLCT before treatment initiation, with reconstruction of IC and Zeffective maps. Final analysis included 115 participants (86 men, 29 women; median age, 65.1 years), incorporating 313 target lesions that were clinically selected using RECIST version 1.1 on arterial-phase acquisitions of the chest and abdomen. Semiautomatic volumetric segmentation was performed of the target lesions. Pixels from all lesions were combined to a single histogram per patient. Median IC and Zeffective of the combined histograms were recorded. Measurements above and below the cohort median values were considered high and low, respectively. Univariable associations were explored between IC and Zeffective, with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Multivariable associations were explored between IC and ORR, PFS, and OS, adjusting for treatment (tyrosine kinase inhibitor versus checkpoint immunotherapy) and significant univariable predictors [including tumor histology and International mRCC Database Consortium (IMDC) risk factors]. Results: At baseline, median IC was 2.26 mg/ml, and median Zeffective was 8.49. In univariable analysis, high IC and high Zeffective were associated with better ORR (both OR=4.35, p=.001), better PFS (both HR=0.51, p=.004), and better OS (both HR=0.38, p<.001). In multivariable models, high IC independently predicted better ORR (OR=4.35, p=.001), better PFS (HR=0.51, p=.004), and better OS (HR=0.37, p<.001); neutrophilia independently predicted worse PFS (HR=2.10, p=.004) and worse OS (HR=2.28, p=.003). The estimated c-index for predicting OS using IMDC risk factors was 0.650, versus 0.687 when incorporating high attention and 0.692 when incorporating high IC or high Zeffective. Conclusions: High IC and high Zeffective are significant predictors of better treatment response and survival in mRCC. Clinical impact: Baseline DLCT parameters may improve current mRCC prognostic models.

Prediction of Inflammatory Breast Cancer Survival Outcomes Using Computed Tomography-Based Texture Analysis

Background: Although inflammatory breast cancer (IBC) has poor overall survival (OS), there is little information about using imaging features for predicting the prognosis. Computed tomography (CT)-based texture analysis, a non-invasive technique to quantify tumor heterogeneity, could be a potentially useful imaging biomarker. The aim of the article was to investigate the usefulness of chest CT-based texture analysis to predict OS in IBC patients.

Methods: Of the 3,130 patients with primary breast cancers between 2006 and 2016, 104 patients (3.3%) with IBC were identified. Among them, 98 patients who underwent pre-treatment contrast-enhanced chest CT scans, got treatment in our institution, and had a follow-up period of more than 2 years were finally included for CT-based texture analysis. Texture analysis was performed on CT images of 98 patients, using commercially available software by two breast radiologists. Histogram-based textural features, such as quantification of variation in CT attenuation (mean, standard deviation, mean of positive pixels [MPP], entropy, skewness, and kurtosis), were recorded. To dichotomize textural features for survival analysis, receiver operating characteristic curve analysis was used to determine cutoff points. Clinicopathologic variables, such as age, node stage, metastasis stage at the time of diagnosis, hormonal receptor positivity, human epidermal growth factor receptor 2 positivity, and molecular subtype, were assessed. A Cox proportional hazards model was used to determine the association of textural features and clinicopathologic variables with OS.

Results: During a mean follow-up period of 47.9 months, 41 of 98 patients (41.8%) died, with a median OS of 20.0 months. The textural features of lower mean attenuation, standard deviation, MPP, and entropy on CT images were significantly associated with worse OS, as was the M1 stage among clinicopathologic variables (all P-values < 0.05). In multivariate analysis, lower mean attenuation (hazard ratio [HR], 3.26; P = 0.003), lower MPP (HR, 3.03; P = 0.002), and lower entropy (HR, 2.70; P = 0.009) on chest CT images were significant factors independent from the M1 stage for predicting worse OS.

Conclusions: Lower mean attenuation, MPP, and entropy on chest CT images predicted worse OS in patients with IBC, suggesting that CT-based texture analysis provides additional predictors for OS.

Addressing intimate partner violence during the COVID-19 pandemic and beyond: how radiologists can make a difference

Faced with the COVID-19 pandemic, many countries both in Europe and across the world implemented strict stay-at-home orders. These measures helped to slow the spread of the coronavirus but also led to increased mental and physical health issues for the domestically confined population, including an increase in the occurrence of intimate partner violence (IPV) in many countries. IPV is defined as behavior that inflicts physical, psychological, or sexual harm within an intimate relationship. We believe that as radiologists, we can make a difference by being cognizant of this condition, raising an alert when appropriate and treating suspected victims with care and empathy. The aim of this Special Report is to raise awareness of IPV among radiologists and to suggest strategies by which to identify and support IPV victims. KEY POINTS: • The COVID-19 pandemic led to a marked increase in the number of intimate partner violence (IPV) cases, potentially leading to increased emergency department visits and radiological examinations. • Most IPV-related fractures affect the face, fingers, and upper trunk, and may easily be misinterpreted as routine trauma. • Radiologists should carefully review the medical history of suspicious cases, discuss the suspicion with the referring physician, and proactively engage in a private conversation with the patient, pointing to actionable resources for IPV victims.

Intimate partner violence crisis in the COVID-19 pandemic: how can radiologists make a difference?

• The COVID-19 crisis resulted in a variety of physical and mental health issues beyond the viral infection itself, as indicated by an increase in domestic violence.

• Radiologists should be aware of typical intimate partner violence (IPV) injury patterns, actively ask potential IPV victims about the cause of injury, and be familiar with support systems for IPV victims of their institutions.

• Emergency and radiology departments should review their protocols for identifying and supporting IPV victims, and train their staff to work together to implement these measures during and beyond the COVID-19 crisis.

Use of Precision Imaging in the Evaluation of Pancreas Cancer

Pancreas cancer is an aggressive and fatal disease that will become one of the leading causes of cancer mortality by 2030. An all-out effort is underway to better understand the basic biologic mechanisms of this disease ranging from early development to metastatic disease. In order to change the course of this disease, diagnostic radiology imaging may play a vital role in providing a precise, noninvasive method for early diagnosis and assessment of treatment response. Recent progress in combining medical imaging, advanced image analysis and artificial intelligence, termed radiomics, can offer an innovate approach in detecting the earliest changes of tumor development as well as a rapid method for the detection of response. In this chapter, we introduce the principles of radiomics and demonstrate how it can provide additional information into tumor biology, early detection, and response assessments advancing the goals of precision imaging to deliver the right treatment to the right person at the right time.