Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section

Recent advances in large language models have led to renewed interest in natural language processing in healthcare using the free text of clinical notes. One distinguishing characteristic of clinical notes is their long time span over multiple long documents. The unique structure of clinical notes creates a new design choice: when the context length for a language model predictor is limited, which part of clinical notes should we choose as the input? Existing studies either choose the inputs with domain knowledge or simply truncate them. We propose a framework to analyze the sections with high predictive power. Using MIMIC-III, we show that: 1) predictive power distribution is different between nursing notes and discharge notes and 2) combining different types of notes could improve performance when the context length is large. Our findings suggest that a carefully selected sampling function could enable more efficient information extraction from clinical notes.Comment: Our code is publicly available on GitHub (https://github.com/nyuolab/EfficientTransformer

Confounding variables can degrade generalization performance of radiological deep learning models

Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis in real-world clinical settings, we must verify their ability to generalize across a variety of hospital systems. A cross-sectional design was used to train and evaluate pneumonia screening CNNs on 158,323 chest x-rays from NIH (n=112,120 from 30,805 patients), Mount Sinai (42,396 from 12,904 patients), and Indiana (n=3,807 from 3,683 patients). In 3 / 5 natural comparisons, performance on chest x-rays from outside hospitals was significantly lower than on held-out x-rays from the original hospital systems. CNNs were able to detect where an x-ray was acquired (hospital system, hospital department) with extremely high accuracy and calibrate predictions accordingly. The performance of CNNs in diagnosing diseases on x-rays may reflect not only their ability to identify disease-specific imaging findings on x-rays, but also their ability to exploit confounding information. Estimates of CNN performance based on test data from hospital systems used for model training may overstate their likely real-world performance

Alterations of metabolic genes and metabolites in cancer

Altered metabolic regulation has long been observed in human cancer and broadly used in the clinic for tumor detection. Two recent findings—the direct regulation of metabolic enzymes by frequently mutated cancer genes and frequent mutations of several metabolic enzymes themselves in cancer—have renewed interest in cancer metabolism. Supporting a causative role of altered metabolic enzymes in tumorigenesis, abnormal levels of several metabolites have been found to play a direct role in cancer development. The alteration of metabolic genes and metabolites offer not only new biomarkers for diagnosis and prognosis, but also potential new targets for cancer therapy

CyberKnife with Tumor Tracking: An Effective Treatment for High-Risk Surgical Patients with Stage I Non-Small Cell Lung Cancer

Published data suggests that wedge resection for stage I non-small cell lung cancer (NSCLC) is associated with improved overall survival compared to stereotactic body radiation therapy. We report CyberKnife outcomes for high-risk surgical patients with biopsy-proven stage I NSCLC. PET/CT imaging was completed for staging. Three-to-five gold fiducial markers were implanted in or near tumors to serve as targeting references. Gross tumor volumes (GTVs) were contoured using lung windows; the margins were expanded by 5 mm to establish the planning treatment volume (PTV). Treatment plans were designed using a mean of 156 pencil beams. Doses delivered to the PTV ranged from 42 to 60 Gy in three fractions. The 30 Gy isodose contour extended at least 1 cm from the GTV to eradicate microscopic disease. Treatments were delivered using the CyberKnife system with tumor tracking. Examination and PET/CT imaging occurred at 3 month follow-up intervals. Forty patients (median age 76) with a median maximum tumor diameter of 2.6 cm (range, 1.4–5.0 cm) and a mean post-bronchodilator percent predicted forced expiratory volume in 1 s (FEV1) of 57% (range, 21–111%) were treated. A median dose of 48 Gy was delivered to the PTV over 3–13 days (median, 7 days). The 30 Gy isodose contour extended a mean 1.9 cm from the GTV. At a median 44 months (range, 12–72 months) follow-up, the 3 year Kaplan–Meier locoregional control and overall survival estimates compare favorably with contemporary wedge resection outcomes at 91 and 75%, respectively. CyberKnife is an effective treatment approach for stage I NSCLC that is similar to wedge resection, eradicating tumors with 1–2 cm margins in order to preserve lung function. Prospective randomized trials comparing CyberKnife with wedge resection are necessary to confirm equivalence

The Value of the History and Physical for Patients with Newly Diagnosed Brain Metastases Considering Radiosurgery

Background: For patients with brain metastases, systemic disease burden has historically been accepted as a major determinant of overall survival (OS). However, less research has focused on specific history and physical findings made by clinicians and how such findings pertain to patient outcomes at a given time point. The aim of this study is to determine how the initial clinical assessment of patients with brain metastases, as part of the history and physical at the time of consultation, correlates with patient prognosis.Methods: We evaluated a prospective, multi-institutional database of 1523 brain metastases in 507 patients who were treated with radiosurgery (Gamma Knife or CyberKnife) from 2001-2014. Relevant history of present illness (HPI) and past medical history (PMH) variables included comorbidities, Eastern Cooperative Oncology Group (ECOG) performance status, and seizure history. Physical exam findings included a sensory exam, motor exam, and cognitive function. Univariate and multivariate Cox regression analyses were used to identify predictors of OS.Results: 294 patients were included in the final analysis with a median OS of 10.8 months (95% C.I., 7.8-13.7 months). On univariate analysis, significant HPI predictors of OS included age, primary diagnosis, performance status, extracranial metastases, systemic disease status, and history of surgery. Significant predictors of OS from the PMH included cardiac, vascular, and infectious comorbidities. On a physical exam, findings consistent with cognitive deficits were predictive of worse OS. However, motor deficits or changes in vision were not predictive of worse OS. In the multivariate Cox regression analysis, predictors of worse OS were primary diagnosis (p=0.002), ECOG performance status (OR 1.73, p<0.001), and presence of extracranial metastases (OR 1.22, p=0.009).Conclusion: Neurologic deficits and systemic comorbidities noted at presentation are not associated with worse overall prognosis for patients with brain metastases undergoing radiosurgery. When encountering new patients with brain metastases, the most informative patient-related characteristics that determine prognosis remain performance status, primary diagnosis, and extent of extracranial disease

CyberKnife with Tumor Tracking: An Effective Treatment for High-Risk Surgical Patients with Single Peripheral Lung Metastases

Standard treatment for operable patients with single peripheral lung metastases is metastasectomy. We report mature CyberKnife outcomes for high-risk surgical patients with biopsy proven single peripheral lung metastases. Twenty-four patients (median age 73 years) with a mean maximum tumor diameter of 2.5 cm (range, 0.8–4.5 cm) were treated over a 6-year period extending from September 2004 to September 2010 and followed for a minimum of 1 year or until death. A mean dose of 52 Gy (range, 45–60 Gy) was delivered to the prescription isodose line in three fractions over a 3–11 day period (mean, 7 days). At a median follow-up of 20 months, the 2-year Kaplan–Meier local control and overall survival rates were 87 and 50%, respectively. CyberKnife with fiducial tracking is an effective treatment for high-risk surgical patients with single small peripheral lung metastases. Trials comparing CyberKnife with metastasectomy for operable patients are necessary to confirm equivalence

Stereotactic radiosurgery for single brain metastases from non-small cell lung cancer: progression of extracranial disease correlates with distant intracranial failure

BackgroundLimited data exist regarding management of patients with a single brain lesion with extracranial disease due to non-small cell lung cancer (NSCLC).MethodsEighty-eight consecutive patients with a single brain lesion from NSCLC in the presence of extracranial disease were treated with stereotactic radiosurgery (SRS) alone. Local control (LC), distant intracranial failure (DIF), overall survival (OS), and toxicity were assessed. The logrank test was used to identify prognostic variables.ResultsMedian OS was 10.6 months. One-year DIF was 61%; LC 89%. Treatments were delivered in 1-5 fractions to median BED10 = 60Gy. Five patients developed radionecrosis. Factors associated with shortened OS included poor performance status (PS) (p = 0.0002) and higher Recursive Partitioning Analysis class (p = 0.017). For patients with PS 0, median survival was 22 months. DIF was associated with systemic disease status (progressive vs. stable) (p = 0.0001), as was BED (p = 0.021) on univariate analysis, but only systemic disease (p = 0.0008) on multivariate analysis.ConclusionsThis study identifies a patient population that may have durable intracranial control after treatment with SRS alone. These data support the need for prospective studies to optimize patient selection for up-front SRS and to characterize the impact of DIF on patients’ quality of life