Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA.

Circulating tumour DNA (ctDNA) can be used to detect and profile residual tumour cells persisting after curative intent therapy1. The study of large patient cohorts incorporating longitudinal plasma sampling and extended follow-up is required to determine the role of ctDNA as a phylogenetic biomarker of relapse in early-stage non-small-cell lung cancer (NSCLC). Here we developed ctDNA methods tracking a median of 200 mutations identified in resected NSCLC tissue across 1,069 plasma samples collected from 197 patients enrolled in the TRACERx study2. A lack of preoperative ctDNA detection distinguished biologically indolent lung adenocarcinoma with good clinical outcome. Postoperative plasma analyses were interpreted within the context of standard-of-care radiological surveillance and administration of cytotoxic adjuvant therapy. Landmark analyses of plasma samples collected within 120 days after surgery revealed ctDNA detection in 25% of patients, including 49% of all patients who experienced clinical relapse; 3 to 6 monthly ctDNA surveillance identified impending disease relapse in an additional 20% of landmark-negative patients. We developed a bioinformatic tool (ECLIPSE) for non-invasive tracking of subclonal architecture at low ctDNA levels. ECLIPSE identified patients with polyclonal metastatic dissemination, which was associated with a poor clinical outcome. By measuring subclone cancer cell fractions in preoperative plasma, we found that subclones seeding future metastases were significantly more expanded compared with non-metastatic subclones. Our findings will support (neo)adjuvant trial advances and provide insights into the process of metastatic dissemination using low-ctDNA-level liquid biopsy.

Long-Term Neurocognitive Outcomes in Sagittal Synostosis: The Impact of Reoperation.

INTRODUCTION: Optimal age at surgery in nonsyndromic sagittal craniosynostosis continues to be debated. Previous reports suggest that earlier age at whole vault cranioplasty more frequently requires reoperation. It is unknown, however, whether reoperation affects neurocognitive outcome. This study examined the impact of reoperation on neurocognitive outcome in children with nonsyndromic sagittal craniosynostosis using comprehensive neurocognitive testing. METHODS: Forty-seven school-age children (age 5-16 years) with nonsyndromic sagittal craniosynostosis who underwent whole-vault cranioplasty were included in this analysis. Participants were administered a battery of standardized neuropsychological testing to measure neurocognitive outcomes. RESULTS: Thirteen of the 47 participants underwent reoperation (27.7%); 11 out of the 13 reoperations were minor revisions while 2 reoperations were cranioplasties. Reoperation rate was not statistically different between patients who had earlier surgery (at age ≤6 months) versus later surgery (at age >6 months) (P > 0.05). Nonreoperated patients who had only one later-in-life surgery did not perform statistically better than reoperated patients on any outcome measure of neurocognitive function, including IQ, academic achievement, visuomotor integration, executive function, and behavior. Comparing reoperated earlier surgery patients with nonreoperated later surgery patients, reoperated earlier surgery patients had higher full-scale and verbal IQ (P < 0.05), scored higher on word reading, reading comprehension, spelling, numerical operations, and visuomotor integration (P < 0.05), and had fewer indicators of suspected learning disabilities (P < 0.01) compared to nonreoperated later surgery patients. CONCLUSION: Reoperation rate after whole vault cranioplasty was 27.7%, with few cases of repeat cranioplasty (4.2% of all patients). Reoperation was not associated with worse neurocognitive outcome. Reoperated earlier surgery patients in fact performed better in IQ, academic achievement and visuomotor integration when compared to nonreoperated later surgery patients.

Hemolytic Anemia and Gastric Carcinoid in a Russian Seafarer: Highlighting the Role of Diagnostic Technologies in Modern Clinical Practice.

Vitamin B-12 deficiency, most commonly due to pernicious anemia, can cause intramedullary hemolysis. The pathogenesis is thought to be due to increased membrane rigidity and reduced red blood cell elasticity, which predisposes the patient to hemolysis and microangiopathic hemolytic anemia. In this article, we discuss a Russian engineer who worked aboard a petroleum tanker that presented from his ship with profound B-12 deficiency, microangiopathic anemia, elevated lactate dehydrogenase levels, low haptoglobin, and reticulocyte count in the setting of normal renal and neurologic function. The patient traveled around the world seven months of the year for work and had occupational exposure to fluorinated hydrocarbons. Extensive diagnostic work-up, including endoscopic biopsy, and a radio-labeled octreotide scan was performed. The patient was found to have autoimmune gastritis and a gastric carcinoid tumor. With assistance from his global health insurance provider and a local hospital near his hometown in Russia, care was coordinated to be transitioned there with a plan for repeat endoscopy and mapping biopsies to determine the extent of his tumor burden. This study adds to the now growing base of literature describing this atypical presentation of pernicious anemia with normal neurologic function and underscores the importance of screening for B-12 deficiency in these patients. It also highlights the increased risk of gastric carcinoids in patients with autoimmune gastritis. With the collaboration of different medical specialists, the full gamut of medical technology was utilized in the care of the patient. This included in vitro diagnostics, advanced endoscopic tools, pathology, and radio-isotope based imaging studies.

Foundation model for cancer imaging biomarkers.

Foundation models in deep learning are characterized by a single large-scale model trained on vast amounts of data serving as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labelled datasets are often scarce. Here, we developed a foundation model for cancer imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of cancer imaging-based biomarkers. We found that it facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed conventional supervised and other state-of-the-art pretrained implementations on downstream tasks, especially when training dataset sizes were very limited. Furthermore, the foundation model was more stable to input variations and showed strong associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering new imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.

Large language models to identify social determinants of health in electronic health records.

Social determinants of health (SDoH) play a critical role in patient outcomes, yet their documentation is often missing or incomplete in the structured data of electronic health records (EHRs). Large language models (LLMs) could enable high-throughput extraction of SDoH from the EHR to support research and clinical care. However, class imbalance and data limitations present challenges for this sparsely documented yet critical information. Here, we investigated the optimal methods for using LLMs to extract six SDoH categories from narrative text in the EHR: employment, housing, transportation, parental status, relationship, and social support. The best-performing models were fine-tuned Flan-T5 XL for any SDoH mentions (macro-F1 0.71), and Flan-T5 XXL for adverse SDoH mentions (macro-F1 0.70). Adding LLM-generated synthetic data to training varied across models and architecture, but improved the performance of smaller Flan-T5 models (delta F1 + 0.12 to +0.23). Our best-fine-tuned models outperformed zero- and few-shot performance of ChatGPT-family models in the zero- and few-shot setting, except GPT4 with 10-shot prompting for adverse SDoH. Fine-tuned models were less likely than ChatGPT to change their prediction when race/ethnicity and gender descriptors were added to the text, suggesting less algorithmic bias (p < 0.05). Our models identified 93.8% of patients with adverse SDoH, while ICD-10 codes captured 2.0%. These results demonstrate the potential of LLMs in improving real-world evidence on SDoH and assisting in identifying patients who could benefit from resource support.

Body Composition in Advanced Non-Small Cell Lung Cancer Treated With Immunotherapy.

Importance: The association between body composition (BC) and cancer outcomes is complex and incompletely understood. Previous research in non-small-cell lung cancer (NSCLC) has been limited to small, single-institution studies and yielded promising, albeit heterogeneous, results. Objectives: To evaluate the association of BC with oncologic outcomes in patients receiving immunotherapy for advanced or metastatic NSCLC. Design, Setting, and Participants: This comprehensive multicohort analysis included clinical data from cohorts receiving treatment at the Dana-Farber Brigham Cancer Center (DFBCC) who received immunotherapy given alone or in combination with chemotherapy and prospectively collected data from the phase 1/2 Study 1108 and the chemotherapy arm of the phase 3 MYSTIC trial. Baseline and follow-up computed tomography (CT) scans were collected and analyzed using deep neural networks for automatic L3 slice selection and body compartment segmentation (skeletal muscle [SM], subcutaneous adipose tissue [SAT], and visceral adipose tissue). Outcomes were compared based on baseline BC measures or their change at the first follow-up scan. The data were analyzed between July 2022 and April 2023. Main Outcomes and Measures: Hazard ratios (HRs) for the association of BC measurements with overall survival (OS) and progression-free survival (PFS). Results: A total of 1791 patients (878 women [49%]) with NSCLC were analyzed, of whom 487 (27.2%) received chemoimmunotherapy at DFBCC (DFBCC-CIO), 825 (46.1%) received ICI monotherapy at DFBCC (DFBCC-IO), 222 (12.4%) were treated with durvalumab monotherapy on Study 1108, and 257 (14.3%) were treated with chemotherapy on MYSTIC; median (IQR) ages were 65 (58-74), 66 (57-71), 65 (26-87), and 63 (30-84) years, respectively. A loss in SM mass, as indicated by a change in the L3 SM area, was associated with worse oncologic outcome across patient groups (HR, 0.59 [95% CI, 0.43-0.81] and 0.61 [95% CI, 0.47-0.79] for OS and PFS, respectively, in DFBCC-CIO; HR, 0.74 [95% CI, 0.60-0.91] for OS in DFBCC-IO; HR, 0.46 [95% CI, 0.33-0.64] and 0.47 [95% CI, 0.34-0.64] for OS and PFS, respectively, in Study 1108; HR, 0.76 [95% CI, 0.61-0.96] for PFS in the MYSTIC trial). This association was most prominent among male patients, with a nonsignificant association among female patients in the MYSTIC trial and DFBCC-CIO cohorts on Kaplan-Meier analysis. An increase of more than 5% in SAT density, as quantified by the average CT attenuation in Hounsfield units of the SAT compartment, was associated with poorer OS in 3 patient cohorts (HR, 0.61 [95% CI, 0.43-0.86] for DFBCC-CIO; HR, 0.62 [95% CI, 0.49-0.79] for DFBCC-IO; and HR, 0.56 [95% CI, 0.40-0.77] for Study 1108). The change in SAT density was also associated with PFS for DFBCC-CIO (HR, 0.73; 95% CI, 0.54-0.97). This was primarily observed in female patients on Kaplan-Meier analysis. Conclusions and Relevance: The results of this multicohort study suggest that loss in SM mass during systemic therapy for NSCLC is a marker of poor outcomes, especially in male patients. SAT density changes are also associated with prognosis, particularly in female patients. Automated CT-derived BC measurements should be considered in determining NSCLC prognosis.

Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging.

Foundation models represent a recent paradigm shift in deep learning, where a single large-scale model trained on vast amounts of data can serve as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labeled datasets are often scarce. Here, we developed a foundation model for imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of imaging-based biomarkers. We found that they facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed their conventional supervised counterparts on downstream tasks. The performance gain was most prominent when training dataset sizes were very limited. Furthermore, foundation models were more stable to input and inter-reader variations and showed stronger associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering novel imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.

Clinical Informatics Approaches to Understand and Address Cancer Disparities.

OBJECTIVES: Disparities in cancer incidence and outcomes across race, ethnicity, gender, socioeconomic status, and geography are well-documented, but their etiologies are often poorly understood and multifactorial. Clinical informatics can provide tools to better understand and address these disparities by enabling high-throughput analysis of multiple types of data. Here, we review recent efforts in clinical informatics to study and measure disparities in cancer. METHODS: We carried out a narrative review of clinical informatics studies related to cancer disparities and bias published from 2018-2021, with a focus on domains such as real-world data (RWD) analysis, natural language processing (NLP), radiomics, genomics, proteomics, metabolomics, and metagenomics. RESULTS: Clinical informatics studies that investigated cancer disparities across race, ethnicity, gender, and age were identified. Most cancer disparities work within clinical informatics used RWD analysis, NLP, radiomics, and genomics. Emerging applications of clinical informatics to understand cancer disparities, including proteomics, metabolomics, and metagenomics, were less well represented in the literature but are promising future research avenues. Algorithmic bias was identified as an important consideration when developing and implementing cancer clinical informatics techniques, and efforts to address this bias were reviewed. CONCLUSIONS: In recent years, clinical informatics has been used to probe a range of data sources to understand cancer disparities across different populations. As informatics tools become integrated into clinical decision-making, attention will need to be paid to ensure that algorithmic bias does not amplify existing disparities. In our increasingly interconnected medical systems, clinical informatics is poised to untap the full potential of multi-platform health data to address cancer disparities.

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality.

PURPOSE: Coronary artery calcium (CAC) quantified on computed tomography (CT) scans is a robust predictor of atherosclerotic coronary disease; however, the feasibility and relevance of quantitating CAC from lung cancer radiotherapy planning CT scans is unknown. We used a previously validated deep learning (DL) model to assess whether CAC is a predictor of all-cause mortality and major adverse cardiac events (MACEs). METHODS: Retrospective analysis of non-contrast-enhanced radiotherapy planning CT scans from 428 patients with locally advanced lung cancer is performed. The DL-CAC algorithm was previously trained on 1,636 cardiac-gated CT scans and tested on four clinical trial cohorts. Plaques ≥ 1 cubic millimeter were measured to generate an Agatston-like DL-CAC score and grouped as DL-CAC = 0 (very low risk) and DL-CAC ≥ 1 (elevated risk). Cox and Fine and Gray regressions were adjusted for lung cancer and cardiovascular factors. RESULTS: The median follow-up was 18.1 months. The majority (61.4%) had a DL-CAC ≥ 1. There was an increased risk of all-cause mortality with DL-CAC ≥ 1 versus DL-CAC = 0 (adjusted hazard ratio, 1.51; 95% CI, 1.01 to 2.26; P = .04), with 2-year estimates of 56.2% versus 45.4%, respectively. There was a trend toward increased risk of major adverse cardiac events with DL-CAC ≥ 1 versus DL-CAC = 0 (hazard ratio, 1.80; 95% CI, 0.87 to 3.74; P = .11), with 2-year estimates of 7.3% versus 1.2%, respectively. CONCLUSION: In this proof-of-concept study, CAC was effectively measured from routinely acquired radiotherapy planning CT scans using an automated model. Elevated CAC, as predicted by the DL model, was associated with an increased risk of mortality, suggesting a potential benefit for automated cardiac risk screening before cancer therapy begins.

Mean Heart Dose Is an Inadequate Surrogate for Left Anterior Descending Coronary Artery Dose and the Risk of Major Adverse Cardiac Events in Lung Cancer Radiation Therapy.

PURPOSE: Mean heart dose (MHD) over 10 Gy and left anterior descending (LAD) coronary artery volume (V) receiving 15 Gy (V15Gy) greater than 10% can significantly increase the risk of major adverse cardiac events (MACE) in patients with non-small cell lung cancer (NSCLC). We sought to characterize the discordance between MHD and LAD dose and the association of this classification on the risk of MACE after radiation therapy. METHODS AND MATERIALS: The coefficient of determination for MHD and LAD V15Gy was calculated in this retrospective analysis of 701 patients with locally advanced NSCLC treated with radiation therapy. Four groups were defined on the basis of high or low MHD (≥10 Gy vs <10 Gy) and LAD V15Gy (≥10% vs <10%). MACE (unstable angina, heart failure, myocardial infarction, coronary revascularization, and cardiac death) cumulative incidence was estimated, and Fine and Gray regressions were performed. RESULTS: The proportion of variance in LAD V15Gy predictable from MHD was only 54.5% (R2 = 0.545). There was discordance (where MHD was high [≥10 Gy] and LAD low [V15Gy < 10%], or vice versa) in 23.1% of patients (n = 162). Two-year MACE estimates were 4.2% (MHDhigh/LADlow), 7.6% (MHDhigh/LADhigh), 1.8% (MHDlow/LADlow), and 13.0% (MHDlow/LADhigh). Adjusting for pre-existing coronary heart disease and other prognostic factors, MHDhigh/LADlow (subdistribution hazard ratio [SHR], 0.34; 95% CI, 0.13-0.93; P = .036) and MHDlow/LADlow (SHR, 0.24; 95% CI, 0.10-0.53; P < .001) were associated with a significantly reduced risk of MACE. CONCLUSIONS: MHD is insufficient to predict LAD V15Gy with confidence. When MHD and LAD V15Gy dose exposure is discordant, isolated low LAD V15Gy significantly reduces the risk of MACE in patients with locally advanced NSCLC after radiation therapy, suggesting that the validity of whole heart metrics for optimally predicting cardiac events should be reassessed.

Deep learning classification of lung cancer histology using CT images.

Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.

Statin Use, Heart Radiation Dose, and Survival in Locally Advanced Lung Cancer.

PURPOSE: Patients with locally advanced non-small cell lung cancer (LA-NSCLC) have a high prevalence of pre-existing coronary heart disease and face excess cardiac risk after thoracic radiation therapy. We sought to assess whether statin therapy is a predictor of overall survival (OS) after thoracic radiation therapy. METHODS AND MATERIALS: We performed a retrospective analysis of 748 patients with LA-NSCLC treated with thoracic radiation therapy, using Kaplan-Meier OS estimates and Cox regression. RESULTS: Statin use among high cardiac risk patients (Framingham risk ≥20% or pre-existing coronary heart disease; n = 496) was 51.2%. After adjustment for baseline cardiac risk and other prognostic factors, statin therapy was associated with a significantly increased risk of all-cause mortality (adjusted hazard ratio, 1.39; 95% confidence interval [CI], 1.00-1.91; P = .048) but not major adverse cardiac events (adjusted hazard ratio, 1.18; 95% CI, 0.52-2.68; P = .69). Among statin-naïve patients, mean heart dose ≥10 Gy versus <10 Gy was associated with a significantly increased risk of all-cause mortality (hazard ratio, 1.32; 95% CI, 1.04-1.68; P = .022), with 2-year OS estimates of 46.9% versus 60.0%, respectively. However, OS did not differ by heart dose among patients on statin therapy (hazard ratio, 1.00; 95% CI, 0.76-1.32; P = 1.00; P-interaction = .031), with 2-year OS estimates of 46.9% versus 50.3%, respectively. CONCLUSIONS: Among patients with LA-NSCLC, only half of statin-eligible high cardiac risk patients were on statin therapy, reflecting the highest cardiac risk level of our cohort. Statin use was an independent predictor of all-cause mortality but not major adverse cardiac events. Elevated mean heart dose (≥10 Gy) was associated with increased risk of all-cause mortality in statin-naïve patients but not among those on statin therapy, identifying a group of patients in which early intervention with statins may mitigate the deleterious effects of high heart radiation therapy dose. This warrants evaluation in prospective trials.

Association of Left Anterior Descending Coronary Artery Radiation Dose With Major Adverse Cardiac Events and Mortality in Patients With Non-Small Cell Lung Cancer.

IMPORTANCE: Radiotherapy accelerates coronary heart disease (CHD), but the dose to critical cardiac substructures has not been systematically studied in lung cancer. OBJECTIVE: To examine independent cardiac substructure radiotherapy factors for major adverse cardiac events (MACE) and all-cause mortality in patients with locally advanced non-small cell lung cancer (NSCLC). DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort analysis of 701 patients with locally advanced NSCLC treated with thoracic radiotherapy at Harvard University-affiliated hospitals between December 1, 2003, and January 27, 2014, was performed. Data analysis was conducted between January 12, 2019, and July 22, 2020. Cardiac substructures were manually delineated. Radiotherapy dose parameters (mean, maximum, and the volume [V, percentage] receiving a specific Gray [Gy] dose in 5-Gy increments) were calculated. Receiver operating curve and cut-point analyses estimating MACE (unstable angina, heart failure hospitalization or urgent visit, myocardial infarction, coronary revascularization, and cardiac death) were performed. Fine and Gray and Cox regressions were adjusted for preexisting CHD and other prognostic factors. MAIN OUTCOMES AND MEASURES: MACE and all-cause mortality. RESULTS: Of the 701 patients included in the analysis, 356 were men (50.8%). The median age was 65 years (interquartile range, 57-73 years). The optimal cut points for substructure and radiotherapy doses (highest C-index value) were left anterior descending (LAD) coronary artery V15 Gy greater than or equal to 10% (0.64), left circumflex coronary artery V15 Gy greater than or equal to 14% (0.64), left ventricle V15 Gy greater than or equal to 1% (0.64), and mean total coronary artery dose greater than or equal to 7 Gy (0.62). Adjusting for baseline CHD status and other prognostic factors, an LAD coronary artery V15 Gy greater than or equal to 10% was associated with increased risk of MACE (adjusted hazard ratio, 13.90; 95% CI, 1.23-157.21; P = .03) and all-cause mortality (adjusted hazard ratio, 1.58; 95% CI, 1.09-2.29; P = .02). Among patients without CHD, associations with increased 1-year MACE were noted for LAD coronary artery V15 Gy greater than or equal to 10% (4.9% vs 0%), left circumflex coronary artery V15 Gy greater than or equal to 14% (5.2% vs 0.7%), left ventricle V15 Gy greater than or equal to 1% (5.0% vs 0.4%), and mean total coronary artery dose greater than or equal to 7 Gy (4.8% vs 0%) (all P ≤ .001), but only a left ventricle V15 Gy greater than or equal to 1% increased the risk among patients with CHD (8.4% vs 4.1%; P = .046). Among patients without CHD, 2-year all-cause mortality was increased with an LAD coronary artery V15 Gy greater than or equal to 10% (51.2% vs 42.2%; P = .009) and mean total coronary artery dose greater than or equal to 7 Gy (53.2% vs 40.0%; P = .01). CONCLUSIONS AND RELEVANCE: The findings of this cohort study suggest that optimal cardiac dose constraints may differ based on preexisting CHD. Although the LAD coronary artery V15 Gy greater than or equal to 10% appeared to be an independent estimator of the probability of MACE and all-cause mortality, particularly in patients without CHD, left ventricle V15 Gy greater than or equal to 1% appeared to confer an increased risk of MACE among patients with CHD. These constraints are worthy of further study because there is a need for improved cardiac risk stratification and aggressive risk mitigation strategies.

Cardiac Radiation Dose, Cardiac Disease, and Mortality in Patients With Lung Cancer.

BACKGROUND: Radiotherapy-associated cardiac toxicity studies in patients with locally advanced non-small cell lung cancer (NSCLC) have been limited by small sample size and nonvalidated cardiac endpoints. OBJECTIVES: The purpose of this analysis was to ascertain whether cardiac radiation dose is a predictor of major adverse cardiac events (MACE) and all-cause mortality (ACM). METHODS: This retrospective analysis included 748 consecutive locally advanced NSCLC patients treated with thoracic radiotherapy. Fine and Gray and Cox regressions were used to identify predictors for MACE and ACM, adjusting for lung cancer and cardiovascular prognostic factors, including pre-existing coronary heart disease (CHD). RESULTS: After a median follow-up of 20.4 months, 77 patients developed ≥1 MACE (2-year cumulative incidence, 5.8%; 95% confidence interval [CI]: 4.3% to 7.7%), and 533 died. Mean radiation dose delivered to the heart (mean heart dose) was associated with a significantly increased risk of MACE (adjusted hazard ratio [HR]: 1.05/Gy; 95% CI: 1.02 to 1.08/Gy; p < 0.001) and ACM (adjusted HR: 1.02/Gy; 95% CI: 1.00 to 1.03/Gy; p = 0.007). Mean heart dose (≥10 Gy vs. <10 Gy) was associated with a significantly increased risk of ACM in CHD-negative patients (178 vs. 118 deaths; HR: 1.34; 95% CI: 1.06 to 1.69; p = 0.014) with 2-year estimates of 52.2% (95% CI: 46.1% to 58.5%) versus 40.0% (95% CI: 33.5% to 47.4%); but not among CHD-positive patients (112 vs. 82 deaths; HR: 0.94; 95% CI: 0.70 to 1.25; p = 0.66) with 2-year estimates of 54.6% (95% CI: 46.8% to 62.7%) versus 50.8% (95% CI: 41.5% to 60.9%), respectively (p for interaction = 0.028). CONCLUSIONS: Despite the competing risk of cancer-specific death in locally advanced NSCLC patients, cardiac radiation dose exposure is a modifiable cardiac risk factor for MACE and ACM, supporting the need for early recognition and treatment of cardiovascular events and more stringent avoidance of high cardiac radiotherapy dose.

Laser Thermal Ablation for Metastases Failing Radiosurgery: A Multicentered Retrospective Study.

BACKGROUND: Radiosurgical failure following stereotactic radiosurgery for brain metastases can be attributed to tumor regrowth or radiation necrosis. MRI-guided laser thermal ablation (LTA) therapy has emerged as an option for treatment; however, previous literature demonstrates variable results across centers. OBJECTIVE: To assess the outcomes of LTA in the treatment of metastases failing radiosurgery across multiple centers and to determine if any treatment factors are predictive of outcome. METHODS: Clinical data for 30 patients across 4 centers were retrospectively reviewed. Patients were included if they received LTA therapy following radiosurgical failure due to radiation necrosis or tumor regrowth. Demographics, surgical data, and follow-up imaging and clinical information were collected. Linear regression analyses were performed to determine treatment factors that were associated with post-LTA outcome. RESULTS: The large majority of patients responded favorably to LTA treatment with low complication rates (23%), short length of stay (53% ≤ 2 d) and reductions in perilesional edema (63%). A total of 73.3% of patients stopped steroids and 48% saw improvement of their preoperative symptoms. Patients with better pre-LTA Karnofsky Performance Status had better survival. Patients who had lesions with more perilesional T2 change post-LTA had a better chance of weaning off steroids and obtaining symptomatic relief. CONCLUSION: MRI-guided laser thermal ablation therapy serves as a viable alternative to traditional treatment options for metastatic brain lesions failing radiosurgery. Although this study is limited by size and is retrospective, LTA therapy may result in symptomatic improvement and a more prominent reduction in fluid-attenuated inversion-recovery signal for larger lesions.

Portable microfluidic chip for detection of Escherichia coli in produce and blood.

Pathogenic agents can lead to severe clinical outcomes such as food poisoning, infection of open wounds, particularly in burn injuries and sepsis. Rapid detection of these pathogens can monitor these infections in a timely manner improving clinical outcomes. Conventional bacterial detection methods, such as agar plate culture or polymerase chain reaction, are time-consuming and dependent on complex and expensive instruments, which are not suitable for point-of-care (POC) settings. Therefore, there is an unmet need to develop a simple, rapid method for detection of pathogens such as Escherichia coli. Here, we present an immunobased microchip technology that can rapidly detect and quantify bacterial presence in various sources including physiologically relevant buffer solution (phosphate buffered saline [PBS]), blood, milk, and spinach. The microchip showed reliable capture of E. coli in PBS with an efficiency of 71.8% ± 5% at concentrations ranging from 50 to 4,000 CFUs/mL via lipopolysaccharide binding protein. The limits of detection of the microchip for PBS, blood, milk, and spinach samples were 50, 50, 50, and 500 CFUs/mL, respectively. The presented technology can be broadly applied to other pathogens at the POC, enabling various applications including surveillance of food supply and monitoring of bacteriology in patients with burn wounds.