Journal club: Renal masses detected at abdominal CT: radiologists' adherence to guidelines regarding management recommendations and communication of critical results

A Web-Based Prediction Model for Cancer-Specific Survival of Middle-Aged Patients With Non-metastatic Renal Cell Carcinoma: A Population-Based Study

Background

Renal cell carcinoma (RCC) is one of the most common cancers in middle-aged patients. We aimed to establish a new nomogram for predicting cancer-specific survival (CSS) in middle-aged patients with non-metastatic renal cell carcinoma (nmRCC).

Methods

The clinicopathological information of all patients from 2010 to 2018 was downloaded from the SEER database. These patients were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate COX regression analyses were used to identify independent risk factors for CSS in middle-aged patients with nmRCC in the training set. Based on these independent risk factors, a new nomogram was constructed to predict 1-, 3-, and 5-year CSS in middle-aged patients with nmRCC. Then, we used the consistency index (C-index), calibration curve, and area under receiver operating curve (AUC) to validate the accuracy and discrimination of the model. Decision curve analysis (DCA) was used to validate the clinical application value of the model.

Results

A total of 27,073 patients were included in the study. These patients were randomly divided into a training set (N = 18,990) and a validation set (N = 8,083). In the training set, univariate and multivariate Cox regression analysis indicated that age, sex, histological tumor grade, T stage, tumor size, and surgical method are independent risk factors for CSS of patients. A new nomogram was constructed to predict patients' 1-, 3-, and 5-year CSS. The C-index of the training set and validation set were 0.818 (95% CI: 0.802-0.834) and 0.802 (95% CI: 0.777-0.827), respectively. The 1 -, 3 -, and 5-year AUC for the training and validation set ranged from 77.7 to 80.0. The calibration curves of the training set and the validation set indicated that the predicted value is highly consistent with the actual observation value, indicating that the model has good accuracy. DCA also suggested that the model has potential clinical application value.

Conclusion

We found that independent risk factors for CSS in middle-aged patients with nmRCC were age, sex, histological tumor grade, T stage, tumor size, and surgery. We have constructed a new nomogram to predict the CSS of middle-aged patients with nmRCC. This model has good accuracy and reliability and can assist doctors and patients in clinical decision making.

Maximizing Value While Volumes are Increasing

Radiologists are facing ever increasing volumes while trying to provide value-based care. There are several drivers of increasing volumes: increasing population size, aging population, increased utilization, gaps in evidence-based care, changes in the provider workforce, defensive medicine, and increasing case complexity. Higher volumes result in increased cognitive and systemic errors and contribute to radiologist fatigue and burnout. We discuss several strategies for mitigating high volumes including abbreviated MRI protocols, 24/7 radiologist coverage, reading room assistants, and other strategies to tackle radiologist burnout.

Imaging of Small Renal Masses before and after Thermal Ablation

Thermal ablation of small renal masses is increasingly accepted as an alternative to partial nephrectomy, particularly in patients with multiple comorbidities. Many professional societies support this alternate treatment with updated guidelines. Before performing thermal ablation, it is important to stratify risk and assess technical feasibility by evaluating tumor imaging features such as size, location, and centrality. Routine postablation imaging with CT or MRI is necessary for assessment of residual or recurrent tumor, evidence of complications, or new renal masses outside the ablation zone. The normal spectrum and evolution of findings at CT and MRI include a halo appearance of the ablation zone, ablation zone contraction, and ablation zone calcifications. Tumor recurrence frequently manifests at CT or MRI as new nodular enhancement at the periphery of an expanding ablation zone, although it is normal for the ablation zone to enlarge within the first few months. Recognizing early tumor recurrence is important, as small renal masses are often easily treated with repeat ablations. Potential complications of thermal ablation include vascular injury, urine leak, ureteral stricture, nerve injury, and bowel perforation. The risk of these complications may be related to tumor size and location.^©RSNA, 2019.

Trends in Renal-Cell Carcinoma Incidence and Mortality in the United States in the Last 2 Decades: A SEER-Based Study

BACKGROUND

Renal-cell carcinoma (RCC) is one of the common malignancies in the United States. RCC incidence and mortality have been changing for many reasons. We performed a thorough investigation of incidence and mortality trends of RCC in the United States using the cell Surveillance, Epidemiology, and End Results (SEER) database.

PATIENTS AND METHODS

The 13 SEER registries were accessed for RCC cases diagnosed between 1992 and 2015. Incidence and mortality were calculated by demographic and tumor characteristics. We calculated annual percentage changes of these rates. Rates were expressed as 100,000 person-years.

RESULTS

A total of 104,584 RCC cases were reviewed, with 47,561 deaths. The overall incidence was 11.281 per 100,000 person-years. Incidence increased by 2.421% per year (95% confidence interval, 2.096, 2.747; P < .001) but later became stable since 2008. However, the incidence of clear-cell subtype continued to increase (1.449%; 95% confidence interval, 0.216, 2.697; P = .024). RCC overall mortality rates have been declining since 2001. However, mortality associated with distant RCC only started to decrease in 2012, with an annual percentage change of 18.270% (95% confidence interval, -28.775, -6.215; P = .006).

CONCLUSION

Despite an overall increase in the incidence of RCC, there has been a recent plateau in RCC incidence rates with a significant decrease in mortality.

Natural Language Processing Technologies in Radiology Research and Clinical Applications

The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the heterogeneity of how these data are formatted. Indeed, although there is movement toward structured reporting in radiology (ie, hierarchically itemized reporting with use of standardized terminology), the majority of radiology reports remain unstructured and use free-form language. To effectively "mine" these large datasets for hypothesis testing, a robust strategy for extracting the necessary information is needed. Manual extraction of information is a time-consuming and often unmanageable task. "Intelligent" search engines that instead rely on natural language processing (NLP), a computer-based approach to analyzing free-form text or speech, can be used to automate this data mining task. The overall goal of NLP is to translate natural human language into a structured format (ie, a fixed collection of elements), each with a standardized set of choices for its value, that is easily manipulated by computer programs to (among other things) order into subcategories or query for the presence or absence of a finding. The authors review the fundamentals of NLP and describe various techniques that constitute NLP in radiology, along with some key applications.