Shuntogram utility in predicting future shunt failures.

OBJECTIVE: Shunt failure remains a challenging diagnosis for neurosurgeons, particularly when patient symptoms suggest shunt malfunction but radiographic evidence is lacking. In such situations, shuntograms are sometimes employed to guide medical decision-making. In this study, the authors aimed to investigate the utility of shuntograms in aiding patient management, particularly in terms of a negative result. METHODS: This retrospective single-institution series comprises patients who underwent a shuntogram procedure to evaluate shunt patency over a roughly 6-year period. The medical records of patients were reviewed to determine the findings of the shuntogram procedure, the type of obstruction, and whether a subsequent operation for a shunt revision took place either within 30 days or up to 1 year after the procedure. Statistical analysis was completed by calculating the sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of the shuntogram as compared with the revision surgery. RESULTS: Of the 98 patients who underwent a shuntogram, 95 patients were included in the statistical analysis. A ventriculoperitoneal shunt was assessed in 81% of patients (77/95). The PPV of the procedure was 100%. The NPV for predicting a subsequent shunt revision within 30 days was 68.3% (false-negative rate of 31.7%) or within 365 days was 61.2% (false-negative rate of 38.8%). The sensitivity and specificity of the shuntogram were 55.8% and 100% within 30 days and 51.9% and 100% within 365 days, respectively. The most common intervention at the time of surgery following a negative shuntogram was a valve replacement in 38.5% of patients (10/26). CONCLUSIONS: With an NPV of 68.3% for predicting revision within 30 days in our series of 95 patients, shuntograms remained a useful tool in the clinical decision-making process when evaluating potential shunt failure.

A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma.

Importance: Recommendations for adjuvant therapy after surgical resection of lung adenocarcinoma (LUAD) are based solely on TNM classification but are agnostic to genomic and high-risk clinicopathologic factors. Creation of a prediction model that integrates tumor genomic and clinicopathologic factors may better identify patients at risk for recurrence. Objective: To identify tumor genomic factors independently associated with recurrence, even in the presence of aggressive, high-risk clinicopathologic variables, in patients with completely resected stages I to III LUAD, and to develop a computational machine-learning prediction model (PRecur) to determine whether the integration of genomic and clinicopathologic features could better predict risk of recurrence, compared with the TNM system. Design, Setting, and Participants: This prospective cohort study included 426 patients treated from January 1, 2008, to December 31, 2017, at a single large cancer center and selected in consecutive samples. Eligibility criteria included complete surgical resection of stages I to III LUAD, broad-panel next-generation sequencing data with matched clinicopathologic data, and no neoadjuvant therapy. External validation of the PRecur prediction model was performed using The Cancer Genome Atlas (TCGA). Data were analyzed from 2014 to 2018. Main Outcomes and Measures: The study end point consisted of relapse-free survival (RFS), estimated using the Kaplan-Meier approach. Associations among clinicopathologic factors, genomic alterations, and RFS were established using Cox proportional hazards regression. The PRecur prediction model integrated genomic and clinicopathologic factors using gradient-boosting survival regression for risk group generation and prediction of RFS. A concordance probability estimate (CPE) was used to assess the predictive ability of the PRecur model. Results: Of the 426 patients included in the analysis (286 women [67%]; median age at surgery, 69 [interquartile range, 62-75] years), 318 (75%) had stage I cancer. Association analysis showed that alterations in SMARCA4 (clinicopathologic-adjusted hazard ratio [HR], 2.44; 95% CI, 1.03-5.77; P = .042) and TP53 (clinicopathologic-adjusted HR, 1.73; 95% CI, 1.09-2.73; P = .02) and the fraction of genome altered (clinicopathologic-adjusted HR, 1.03; 95% CI, 1.10-1.04; P = .005) were independently associated with RFS. The PRecur prediction model outperformed the TNM-based model (CPE, 0.73 vs 0.61; difference, 0.12 [95% CI, 0.05-0.19]; P < .001) for prediction of RFS. To validate the prediction model, PRecur was applied to the TCGA LUAD data set (n = 360), and a clear separation of risk groups was noted (log-rank statistic, 7.5; P = .02), confirming external validation. Conclusions and Relevance: The findings suggest that integration of tumor genomics and clinicopathologic features improves risk stratification and prediction of recurrence after surgical resection of early-stage LUAD. Improved identification of patients at risk for recurrence could enrich and enhance accrual to adjuvant therapy clinical trials.

Giant Intracranial Extraaxial Parietal-Occipital Cavernous Hemangioma in an Adolescent.

BACKGROUND: Extraaxial cavernous hemangiomas (EACHs) are an extremely rare form of cerebral vascular malformations, occurring infrequently in children and rarely exceeding 6.0 cm in diameter (labeled as "giant"). Our case study highlights the radiographic, diagnostic, and interventional uncertainties in the management of giant EACHs in a pediatric population. CASE DESCRIPTION: Our patient, a 15-year-old male with 3-month history of hemiparetic symptoms, was shown to have a 9.0-cm extraaxial parietal-occipital mass upon imaging. Differential diagnosis included cavernoma versus meningioma because of similarities in imaging presentation. Pathologic confirmation of EACH was achieved after resection, and the patient experienced positive outcomes including total recovery from his hemiparesis. CONCLUSIONS: EACHs are difficult to characterize radiographically and are often mistaken to be meningiomas before resection and pathologic confirmation. Therefore cavernous hemangiomas should be considered in the differential diagnosis of pediatric extraaxial masses. Additionally, preoperative hemorrhage control efforts should be pursued to reduce adverse outcomes stemming from resection of vascular malformations such as EACHs.