Diagnosis Of Early-stage Cancer Research Articles

Unmet Social Needs and Breast Cancer Screening Utilization and Stage at Presentation

Unmet social needs in local populations may hinder the development of targeted cancer control interventions aimed at improving screening utilization and early-stage breast cancer diagnosis to ultimately improve breast cancer survival disparities. To evaluate if (1) city-funded screening mammography is associated with utilization of screening mammography, (2) unmet social needs are associated with utilization of screening mammography, and (3) unmet social needs are associated with later-stage disease at diagnosis. This cohort study included patients with stages I-IV invasive ductal or lobular carcinoma treated at an academic medical center (including both an underserved safety-net hospital [SNH] and a National Cancer Institute-designated academic cancer center [ACC]) from 2020 to 2023. Eligible patients were aged 18 years or older and able to consent. Data were analyzed between July 2023 and September 2023. The Health Leads Social Needs Screening Toolkit, a screening tool that gathers information on the most common social need domains affecting patient health. Univariable and multivariable logistic regression was utilized to evaluate the following primary outcomes: (1) routine screening mammography and (2) American Joint Committee on Cancer 8th edition clinical stage at presentation. Of the 322 women who completed the Health Leads Social Needs Screening Toolkit, 201 (62%) self-identified as Hispanic, 63 (19%) as non-Hispanic Black, and 63 (19%) as non-Hispanic White. Two hundred fifty-five (76%) patients with access to city-funded screening mammography completed a screening mammogram. Patients who presented to the SNH were more likely to present with late-stage disease compared with early-stage disease (15 of 48 [31%] vs 50 of 274 [18%]; P = .04). On multivariable logistic regression, not completing a screening mammography was associated with having an increasing number of unmet social needs (OR, 0.74; 95% CI, 0.55-0.99; P = .047) and an increasing age at diagnosis (OR, 0.92; 95% CI, 0.89-0.96; P < .001). Moreover, increasing unmet social needs was significantly associated with late-stage diagnosis above and beyond screening mammography (OR, 1.38; 95% CI, 1.01-1.89; P = .04). In this cohort study, access to screening mammography did not translate to utilization of screening mammography, increasing unmet social needs were significantly associated with lower rates of screening mammography, and those with increasing unmet social needs were more likely to present with late-stage disease. This association transcended recruitment site (SNH vs ACC), indicating that patients in either hospital setting may benefit from unmet social needs screening to overcome access to care barriers associated with late-stage disease at diagnosis.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Serum Interleukins 8, 17, and 33 as Potential Biomarkers of Colon Cancer.

This research investigated the serum levels of three interleukins (IL8, IL17A, and IL33) and the possible relationships between them in healthy people and colon cancer patients at different stages. This study involved 82 participants, 42 of whom had colon cancer and 40 were healthy individuals. The cancer patients were classified into four groups according to the TNM staging classification of colon and rectal cancer. Serum levels of the interleukins were measured by the ELISA test. The data were analyzed statistically to compare the demographic characteristics, the interleukin levels across cancer stages, and the correlation between interleukins in both groups. The results showed that women had more early-stage colon cancer diagnoses, while men had more advanced-stage cancer diagnoses. Stage two colon cancer was more common in older people. Younger people, men, and those with early-stage colon cancer had higher levels of interleukins. The levels of IL8 and IL17A were higher in the cancer group, while the level of IL33 was higher in the healthy group. There was a strong correlation between IL8 and IL17A levels in both groups (p = 0.001). IL17A influenced the level of IL33 in the cancer group (p = 0.007). This study suggested that cytokine variation profiles could be useful for detecting colon cancer and predicting its outcome.

Eliminating Medicaid dental benefits and early-stage oral cancer diagnoses.

Despite the importance of regular dental visits for detecting oral cancer, millions of low-income adults lack access to dental services. In July 2009, California eliminated adult Medicaid dental benefits. We tested if this impacted oral cancer detection for Medicaid enrollees. We analyzed Surveillance, Epidemiology, and End Results-Medicaid data, which contains verified Medicaid enrollment status, to estimate a difference-in-differences model. Our design compares the change in early-stage (Stages 0-II) diagnoses before and after dropping dental benefits in California with the change in early-stage diagnoses among eight states that did not change Medicaid dental benefits. Patients were grouped by oropharyngeal cancer (OPC) and non-OPC (oral cavity cancer), type, and the length of Medicaid enrollment. We also assessed if the effect of dropping dental benefits varied by the number of dentists per capita. Dropping Medicaid dental benefits was associated with a 6.5%-point decline in early-stage diagnoses of non-OPC (95% CI = -14.5, -3.2, p = 0.008). This represented a 20% relative reduction from baseline rates. The effect was highest among beneficiaries with 3 months of continuous Medicaid enrollment prior to diagnosis who resided in counties with more dentists per capita. Specifically, dropping dental coverage was associated with a 1.25%-point decline in the probability of early-stage non-OPC diagnoses for every additional dentist per 5000 population (p = 0.006). Eliminating Medicaid dental benefits negatively impacted early detection of cancers of the oral cavity. Continued volatility of Medicaid dental coverage and provider shortages may be further delaying oral cancer diagnoses. Alternative approaches are needed to prevent advanced stage OPC.

Center-Focused Affinity Loss for Class Imbalance Histology Image Classification.

Early-stage cancer diagnosis potentially improves the chances of survival for many cancer patients worldwide. Manual examination of Whole Slide Images (WSIs) is a time-consuming task for analyzing tumor-microenvironment. To overcome this limitation, the conjunction of deep learning with computational pathology has been proposed to assist pathologists in efficiently prognosing the cancerous spread. Nevertheless, the existing deep learning methods are ill-equipped to handle fine-grained histopathology datasets. This is because these models are constrained via conventional softmax loss function, which cannot expose them to learn distinct representational embeddings of the similarly textured WSIs containing an imbalanced data distribution. To address this problem, we propose a novel center-focused affinity loss (CFAL) function that exhibits 1) constructing uniformly distributed class prototypes in the feature space, 2) penalizing difficult samples, 3) minimizing intra-class variations, and 4) placing greater emphasis on learning minority class features. We evaluated the performance of the proposed CFAL loss function on two publicly available breast and colon cancer datasets having varying levels of imbalanced classes. The proposed CFAL function shows better discrimination abilities as compared to the popular loss functions such as ArcFace, CosFace, and Focal loss. Moreover, it outperforms several SOTA methods for histology image classification across both datasets.

Incidental diagnosis of lung cancer on chest CT scan performed for suspected or documented COVID-19 infection

ContextRecent studies have shown a benefit of chest computed tomography (CT scan) in lung cancer screening. The COVID-19 pandemic has led to many chest CT scan performed on a large population. The objective of this study was to describe the incidence and characteristics of lung cancer detected on chest CT scan, outside the framework of a clinical trial, for a suspected or documented COVID-19 infection. MethodsWe conducted a multicenter study, carried out from the analysis of data from the prospective COVID-19 database of the Clinical Data Warehouse of the Greater Paris University Hospitals (AP-HP). We identified the patients who had been diagnosed with a lung cancer, due to a chest CT scan done for a suspected or confirmed COVID-19 infection. The study period was limited to the first two epidemic lockdowns: (03/01/20 - 05/31/20) and (10/10/20 - 11/30/20). ResultsOver the study period, 24 390 patients had at least one chest CT scan. Among them, 72 lung cancer diagnoses were made (incidence 0.30 %; median age 67.4 years old, 50.0 % current smokers, 55.6 % adenocarcinoma). Half of the lung cancer patients (n = 36) did not meet the National Lung Screening Trial inclusion criteria. Twenty-six patients (36.1 %) were diagnosed at an early stage, 25 (34.7 %) of whom received radical curative treatment. Twenty-six patients died during the follow-up (36.1 %) but none in early stages. The median overall survival in lung cancer patients was 693 days [532 – NA]. ConclusionsA large-scale chest CT scan strategy for suspected or documented COVID-19 infection has allowed a significant proportion of early-stage lung cancer diagnosis, all of which have benefited from curative treatment.

Lung Cancer Screening in People With COPD: The Pamplona-IELCAP Experience

IntroductionThe Global Initiative for Obstructive Lung Disease (GOLD) recommends lung cancer screening for patients with Chronic Obstructive Pulmonary Disease (COPD), but data is lacking regarding results of screening in this high-risk population. The main goal of the present work is to explore if lung cancer screening with Low Dose Chest Tomography (LDCT) in people with COPD, allows lung cancer (LC) diagnosis in early stages with survival compatible with curative state. MethodsThis is a post hoc exploratory analysis. Pamplona International Early Lung Cancer Action Program (P-IELCAP) participants with a GOLD defined obstructive pattern (post bronchodilator FEV1/FVC<0.70) were selected for analysis. The characteristics of those who developed LC and their survival are described. A Cox proportional analysis explored the factors associated with LC diagnosis. ResultsEight hundred and sixty-five patients (77% male, 93% in spirometric GOLD stage 1+2) were followed for 102±63 months. LC prevalence was 2.6% at baseline, with an annual LC diagnosis rate of 0.68%. Early-stage tumors predominated (74%) with a median survival (25–75th percentiles) of 139 (76–185) months. Cumulative tobacco exposure, FEV1%, and emphysema were the main predictors of an LC diagnosis. Eight (11%) patients with COPD had a second LC, most of them in early stage (92%), and 6 (8%) had recurrence. Median survival (25–75th percentiles) in these patients was 168 (108–191) months. ConclusionsLung cancer screening of selected high-risk participants with COPD allowed the LC diagnosis in early stages with survival compatible with curative state.

Development of an activity-based ratiometric electrochemical probe of the tumor biomarker γ-glutamyl transpeptidase: Rapid and convenient sensing in whole blood, urine and live-cell samples

γ-Glutamyl transpeptidase (GGT) is a key biomarker for cancer diagnosis and post-treatment surveillance. Currently available methods for sensing GGT show high potential, but face certain challenges including an inability to be used to directly sense analytes in turbid biofluid samples such as whole blood without tedious sample pretreatment. To overcome this issue, activity-based electrochemical probes (GTLP and GTLPOH) were herein developed for a convenient and specific direct targeting of GGT activity in turbid biosamples. Both probes were designed to have GGT catalyze the hydrolysis of the gamma-glutamyl amide moiety of the probe, and result in a self-immolative reaction and concomitant ejection of the masked amino ferrocene reporter. The GTLPOH probe, delivered distinctive key results including high sensitivity, high affinity, a wide detection range of 2–100 U/L, and low LOD of 0.38 U/L against GGT. This probe delivered a precise target for sensing GGT and was free of interference from other electroactive biological species. Furthermore, the GTLPOH probe was employed to monitor and quantify the activity of GGT on the surfaces of tumor cells. The designed sensing method was also validated by the direct quantitative measurement of GGT activity in whole blood and urine samples, and the results were found to be consistent with those of the standard fluorometric assay kit. Thus, GTLPOH is of great significance for its promise as a point-of-care tool for early-stage cancer diagnosis as well as a new drug screening method.

Tumor mineralization-based cancer diagnosis and therapy

Biomineralization is a phenomenon that involves the deposition of inorganic ions onto organic substrates, resulting in the formation of hard tissue materials. Tumor mineralization, on the other hand, encompasses two key aspects: tumor calcification and tumor iron mineralization. The occurrence of spontaneous tumor calcification and regional lymph node calcification in colorectal cancer, lung cancer, and glioblastoma has been established as a favorable prognostic factor in clinical settings. Building upon this understanding, we propose the concept and advance the development of a compound that artificially induces bionic mineralization around the surface of cancer cells. This process has demonstrated exceptional efficacy in inhibiting the growth and metastasis of cervical, breast, and lung tumors. Moreover, it has exhibited outstanding performance in the early-stage diagnosis of cancer. Consequently, we anticipate that this concept holds significant potential for cancer-targeted mineralization therapy and diagnosis, offering a novel avenue for the development of anticancer drugs.

Hepatocellular Carcinoma Detection by Cell Sensor Based on Anti-GPC3 Single-Chain Variable Fragment

Precise identification of hepatocellular carcinoma (HCC) in early stages remains both a challenge and an opportunity for prevention and diagnosis of early liver cancer. Fast-detecting techniques like electrochemical sensing might provide more insight than current clinical methods owing to its high sensitivity, rapid detection, and convenient operation. Here, a single-chain variable fragment (scFv) was designed by retaining the antigen binding active sites of a recombinant humanized monoclonal antibody and modifying with histidine tags, and consequently used for the construction of cell sensor for HCC detection. The customized scFv showed specific binding signature on the membrane of HCC cell and positive correlation with cell concentration using fluorescence confocal microscopy and flow cytometry, respectively. A higher binding affinity of scFv over the original monoclonal antibody was also displayed. After the immobilization onto the screen-printed electrode through the high affinity between histidine and nickel-based nanomaterial, the constructed cell sensor exhibited a wide detection range of 10 2 to 10 7 cells ml −1 and a low detection limit of 2 cells ml −1 with a short incubation time of 5 min. The high selectivity in the liver tissue extract fluid derived from the design strategy was also displayed in the mouse samples. The customized scFv and related cell sensor demonstrated great potential for fast HCC analysis in early-stage cancer diagnosis.

A Novel Dual Layer Cascade Reliability Framework for an Informed and Intuitive Clinician-AI Interaction In Diagnosis of Colorectal Cancer Polyps.

We present a novel Cascade Reliability Framework (CRF) that integrates two independent cascade layers of reliability (i.e., variational temperature scaling and conformal prediction) with a pre-trained Machine Learning (ML) model in order to provide clinicians with a more reliable and tunable tool for early-stage diagnosis of Colorectal Cancer (CRC) polyps. The conformal prediction layer generates predictive sets that are guaranteed to contain the true polyp type with an adjustable error rate tuned by clinicians, while the confidence calibration generates meaningful confidence estimates for each predicted label. These two layers provide additional information and an error-tuning-ability for clinicians to assist them in making informed and intuitive decisions considering the outputs of the pre-trained ML model. Utilizing a novel vision-based tactile sensor and unique 3D-printed CRC polyp phantoms, we evaluated the trustworthiness of the proposed architecture and particularly dual outputs of four different types of CRF models, integrated with two different pre-trained ML models (i.e., ResNet18 and Dilated Residual Network) to highlight the model-agnostic feature of the architecture. To thoroughly assess the performance of the proposed approach, we used reliability diagrams and metrics such as accuracy, coverage, and average set size, while also addressing inter-class performance. Results demonstrate that the calibrated CRF models are well capable of handling non-ideal inputs with noise and blur. Moreover, using the conformal prediction with a user-defined error rate and various experiments, we show how clinicians can intuitively interact with a pre-trained ML model to make informed decisions and minimize the risk of CRC polyps misdiagnoses.

Pristine and aurum-decorated tungsten ditellurides as sensing materials for VOCs detection in exhaled human breath: DFT analysis.

In this research, we employed density functional theory (DFT) to evaluate the sensing capabilities of transition metal-decorated two-dimensional WTe2 TMDs nanosheets toward VOCs such as (acetone, ethanol, methanol, toluene, and formaldehyde) that are exhaled in human breath and can serve as potential biomarkers for detecting specific physiological disorders and also gases interfering in exhaled breath (CO2 and H2O) detection. Au can be physically decorated onto the surface of WTe2. We analyzed the density of states (DOS), adsorption energy, charge transfer, and sensing behavior. The pristine WTe2 monolayer, exhibiting a semiconductor characteristic with a band gap of 0.63 eV, transitions to a metallic state upon Au-decoration, due to its actively stable nature and promising negative adsorption energy value, it triggers the emergence of novel states within the DOS. Computed adsorption energies of VOCs range from -0.08 to -0.57 eV, with greater interaction distances confirming the physisorption behavior of these VOCs biomarkers on Au-WTe2. Ethanol displays greater sensitivity compared to other considered VOCs. Au-WTe2 exhibits promising potential as a viable option for detecting VOCs in breath analysis applications at room temperature, owing to its excellent adsorption capabilities and sensitivity. Overall, our results highlight aurum-decorated tungsten ditelluride's potential as an efficient nano-sensor for detecting VOCs associated with early-stage lung cancer diagnoses.

Recommendations for the diagnosis, monitoring, and treatment of early-stage non-small cell lung cancer in Colombia

Introducción: El cáncer de pulmón de células no pequeñas (CPCNP) de- rivado o no de la exposición crónica al humo por combustión del tabaco representa un problema sanitario global y local dada su alta letalidad, por la rápida progresión de la medicina de precisión y el impacto presupues- tal de las nuevas intervenciones. Dada la notable evolución de los métodos diagnósticos y terapéuticos para el CPCNP, se decidió realizar un consenso orientado a priorizar las necesidades del entorno colombiano, adaptando las recomendaciones de múltiples esfuerzos integrativos previos. Objetivo: Generar recomendaciones para optimizar el manejo integral de los pacientes con CPCNP en Colombia a partir de la discusión estructurada en un grupo focal de expertos incluyendo cirujanos de tórax, oncólogos clínicos, radiólogos, patólogos, neumólogos y genetistas. Dada la transición epidemiológica de la enfermedad se hizo énfasis en las etapas tempranas de la enfermedad. Métodos: Usando una herramienta digital (AGREE II Tool) para la caracterización de información relevante se elaboraron y discutieron las preguntas orientadoras para cada sesión sincrónica. En paralelo se hizo una revisión sistemática de la literatura usando la plataforma SCOPUS con énfasis en la extracción de guías de práctica clínica (GPC), metaanálisis, estudios clínicos y documentos de consenso. Además, se evaluaron y sintetizaron las principales recomendaciones de la International Agency for the Study of Lung Cancer (IASLC), de la American Asociation of Clinical Oncology (ASCO), de la European Society of Medical Oncology (ESMO), la National Comprehensive Cancer Network (NCCN), y del National Institute for Health and Care Excellence (NICE). A partir de las discusiones se seleccionaron 15 ejes primarios para la discusión considerando el tamizaje para poblaciones en riesgo, la estadificación, el tratamiento y el seguimiento. Después de cada discusión virtual se realizaron votaciones y se estimó el nivel de acuerdo para cada una. Resultados: En 5 sesiones grupales se discutieron 15 preguntas orientadoras que derivaron en 82 recomendaciones (8 tamizaje, 11 estadificación, 4 diagnóstico y 59 tratamiento). Setenta y siete obtuvieron un alto nivel de acuerdo (&gt;80 %), 5 un nivel intermedio (30-79 %), y ninguna obtuvo nivel bajo. Finalmente, 67 cumplieron con los dos criterios de aceptación (tamizaje 8, estadificación 11, diagnóstico 3, tratamiento 48). Conclusión: A partir de la revisión sistemática de la literatura y la discusión multidisciplinaria se realizaron recomendaciones orientadas a mejorar el manejo del paciente con CPCNP en el contexto colombiano.

Early-stage diagnosis of bladder cancer using surface-enhanced Raman spectroscopy combined with machine learning algorithms in a rat model

Early diagnosis and accurate assessment of tumor development facilitate early bladder cancer resection and initiation of drug therapy. This study enabled an early, accurate, label-free, noninvasive diagnosis of bladder tumors by analyzing nano-biomarkers in a single drop of urine through surface-enhanced Raman spectroscopy (SERS). In a standard N-butyl-N-4-hydroxybutyl nitrosamine–induced rat model of bladder cancer, cancer stage and polyp tumor development were monitored using a small endoscope with a diameter of 1.2 mm in a minimally invasive manner without the need to kill the rats. Samples were divided into cancer-free, early-stage, and polyp-form cancer. Training data were classified according to micro-cystoscopic 5-aminolevulinic acid fluorescence diagnosis, and specimens were postmortem verified through histopathological analysis. A drop of urine from each sample group was placed on an Au-coated zinc oxide nanoporous chip to filter nano-biomaterials and selectively enhance the Raman signals of nanoscale analytes via SERS. Principal component analysis was used to reduce the dimensionality of the collected Raman spectra, and partial least squares discriminant analysis was used to find diagnostic clusters based on the labeled samples. The combination of SERS and machine learning achieved an accuracy ≥99.6% in diagnosing both early- and polyp-stage bladder tumors. With an area under the receiver operating characteristic curve greater than 0.996, the accuracy of the diagnosis in the rat model suggests that SERS-based diagnostic methods are promising when coupled with machine learning. Low-cost, label-free, and noninvasive surface-enhanced Raman spectra are ideal for developing clinically relevant point-of-care diagnostic techniques.

Novel Nanoprobe with Combined Ultrasonography/Chemical Exchange Saturation Transfer Magnetic Resonance Imaging for Precise Diagnosis of Tumors.

Given that cancer mortality is usually due to a late diagnosis, early detection is crucial to improve the patient's results and prevent cancer-related death. Imaging technology based on novel nanomaterials has attracted much attention for early-stage cancer diagnosis. In this study, a new block copolymer, poly(ethylene glycol)-poly(l-lactide) diblock copolymer (PEG-PLLA), was synthesized by the ring-opening polymerization method and thoroughly characterized using Fourier transform infrared spectroscopy (FT-IR), proton nuclear magnetic resonance spectroscopy (H-NMR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). The obtained PEG-PLLA was used to prepare nanoparticles encapsulated with perfluoropentane and salicylic acid by the emulsion-solvent evaporation method, resulting in a new dual-mode nano-image probe (PEG-PLLA@SA·PFP). The zeta potential and mean diameter of the obtained nanoparticles were measured using dynamic light scattering (DLS) with a Malvern Zetersizer Nano. The in vitro biocompatibility of the PEG-PLLA nanoparticles was evaluated with cell migration, hemolysis, and cytotoxicity assays. Ultrasonic imaging was performed using an ultrasonic imaging apparatus, and chemical exchange saturation transfer (CEST) MRI was conducted on a 7.0 T animal scanner. The results of IR and NMR confirmed that the PEG-PLLA was successfully synthesized. The particle size and negative charge of the nanoparticles were 223.8 ± 2.5 nm and -39.6 ± 1.9 mV, respectively. The polydispersity of the diameter was 0.153 ± 0.020. These nanoparticles possessed good stability at 4 °C for about one month. The results of cytotoxicity, cell migration, and hemolysis assays showed that the carrier material was biocompatible. Finally, PEG-PLLA nanoparticles were able to significantly enhance the imaging effect of tumors by the irradiation of ultrasound and saturation by a radiofrequency pulse, respectively. In conclusion, these nanoparticles exhibit promising dual-mode capabilities for US/CEST MR imaging.

Emerging Microfluidic Tools for Simultaneous Exosomes and Cargo Biosensing in Liquid Biopsy: New Integrated Miniaturized FFF-Assisted Approach for Colon Cancer Diagnosis

The early-stage diagnosis of cancer is a crucial clinical need. The inadequacies of surgery tissue biopsy have prompted a transition to a less invasive profiling of molecular biomarkers from biofluids, known as liquid biopsy. Exosomes are phospholipid bilayer vesicles present in many biofluids with a biologically active cargo, being responsible for cell-to-cell communication in biological systems. An increase in their excretion and changes in their cargo are potential diagnostic biomarkers for an array of diseases, including cancer, and they constitute a promising analyte for liquid biopsy. The number of exosomes released, the morphological properties, the membrane composition, and their content are highly related to the physiological and pathological states. The main analytical challenge to establishing liquid biopsy in clinical practice is the development of biosensors able to detect intact exosomes concentration and simultaneously analyze specific membrane biomarkers and those contained in their cargo. Before analysis, exosomes also need to be isolated from biological fluids. Microfluidic systems can address several issues present in conventional methods (i.e., ultracentrifugation, size-exclusion chromatography, ultrafiltration, and immunoaffinity capture), which are time-consuming and require a relatively high amount of sample; in addition, they can be easily integrated with biosensing systems. A critical review of emerging microfluidic-based devices for integrated biosensing approaches and following the major analytical need for accurate diagnostics is presented here. The design of a new miniaturized biosensing system is also reported. A device based on hollow-fiber flow field-flow fractionation followed by luminescence-based immunoassay is applied to isolate intact exosomes and characterize their cargo as a proof of concept for colon cancer diagnosis.

Social determinants of physicians’ treatment recommendation in early glottic cancer.

65 Background: Glottic cancer is often diagnosed early due to its deleterious effects on the voice. National cancer treatment guidelines recommend larynx preserving treatment, either surgery or radiation, since both modalities have similar rates of local control and recurrence. Without any survival advantage for either treatment modality, physicians may make treatment recommendations based on other clinical or nonclinical factors. Methods: Using the National Cancer Database, a national hospital-based cancer registry data, this retrospective cohort analysis included adult patients, aged 18 years and older, with clinically confirmed diagnosis of early-stage (T1 and T2) glottic cancer between 2004 and 2017 (n = 27,632). The outcome of interest was physicians’ treatment recommendation of surgery or radiation therapy. Main measures included social determinants of health variables, including race/ethnicity, urban/rural status, health insurance status, and income. We further adjusted for covariates, including age and gender. Logistic regression analysis estimated odds of physicians’ recommendation of radiation versus Surgery, based on social determinants of health factors and adjusting for all covariates in the model. Results: More patients received recommendation for radiation treatment vs surgery (78% vs 22%). Female patients were less likely to receive a recommendation of radiation (aOR = 0.90; 95% CI 0.82, 0.98), while Black patients had increased odds of being recommended radiation compared to White patients (aOR = 1.16, 95% CI 1.04, 1.29). Both facility type and location, and patients’ urban vs. rural status were all associated with treatment recommendation, and individuals residing in rural areas had 32% increased odds of being recommendation radiation treatment vs surgery (aOR = 1.32; 95% CI 1.06, 1.64). Conclusions: Radiation treatment is prevalent in early-glottic cancer care, and we found an association between social determinants of health and physician recommendation of treatment. Further research should examine concordance of patient treatment preference vs physician recommendation.

Robotic-assisted navigation system for preoperative lung nodule localization: a pilot study.

Preoperative percutaneous computed tomography (CT)-guided localization of pulmonary nodules plays a pivotal role in the diagnosis and treatment of early-stage lung cancer. However, conventional manual localization techniques have inherent limitations in achieving a high degree of accuracy. Consequently, a novel robotic-assisted navigation system was developed to attain precise localization of small lung nodules. This study aims to investigate the accuracy and safety of this system in clinical applications. Patients with peripheral solitary pulmonary nodules measuring less than 20 mm were enrolled. The robotic-assisted navigation system generated a three-dimensional (3D) model based on the patient's CT images, determining the optimal puncture path. The robotic arm then accurately located the nodule and, following percutaneous puncture, indocyanine green (ICG) was injected. The primary outcome measure was the accuracy of pulmonary nodule localization, while secondary outcomes included the complication rate, procedural duration, and total radiation exposure. A total of 33 nodules were successfully localized using the robotic-assisted navigation system and resected through video-assisted thoracoscopic surgery (VATS). The first-pass success rate was 100%, with a median deviation of 6.1 mm [interquartile range (IQR), 2.5-7.2 mm] between the localizer and the nodule. The median localization time was 25.0 minutes, and the single and cumulative exam dose-length products (DLP) were 534.0 and 1491.0 mGy·cm, respectively. Notably, no observable complications were reported during the procedures. The innovative robotic-assisted navigation system demonstrated satisfactory accuracy and holds promise for improving the percutaneous localization of lung nodules. This method represents a safe and viable alternative to traditional CT-guided manual localization techniques.

Prevalence and timing of chronic obstructive pulmonary disease (COPD) diagnosis among patients newly diagnosed with lung cancer in the US.

118 Background: COPD is one of the most common comorbidities among patients with lung cancer, and an important determinant of their survival. While COPD is frequently under diagnosed in the general population, less is known about the occurrence of COPD underdiagnosis in patients with lung cancer. Objectives: 1) To estimate prevalence and under diagnosis of COPD among patients newly diagnosed with lung cancer. 2) To quantify the association between timing of COPD diagnosis and stage of lung cancer at diagnosis and whether this association is modified by sex, race, and socioeconomic status (SES). Methods: A secondary data analysis was conducted using the SEER-Medicare database. We included Medicare beneficiaries aged 66+years with primary invasive lung cancer diagnosed from 2008 to 2017 who were alive at the time of cancer diagnosis and had continuous Medicare coverage 12 months prior and 3 months after lung cancer diagnosis. Exposure: Comorbid COPD was determined using Medicare claims and classified as early COPD (evidence of COPD related healthcare utilization (HCU) &gt;3 months before lung cancer diagnosis) versus late COPD (evidence of COPD related HCU only during the +/- 3 months from lung cancer diagnosis). Outcome: Early (localized) versus late (regional and distant) stage of lung cancer diagnosis. Statistical analysis: We used generalized linear models to estimate adjusted absolute (prevalence difference PD) and relative (prevalence ratio PR) association. Results: In a population of 185,405 Medicare beneficiaries with lung cancer, 131,230 (71%) had evidence of COPD. Compared to lung cancer patients without COPD, those with COPD were more likely to be younger, male, White, of lower SES, have more comorbidities, higher HCU, and be diagnosed with early stage lung cancer. Patients with early COPD diagnosis constitute 66,867 (51%) of patients with COPD and lung cancer. They were more likely to be older, female, Non-Hispanic White, of lower SES, and to have more comorbidities, acute exacerbation of COPD, and more early-stage lung cancer compared to patients with late COPD diagnosis. We observed a significant positive association between early COPD diagnosis and early-stage lung cancer diagnosis (PR= 1.27, 95% CI= 1.24 to 1.30; PD= 0.062, 95% CI= 0.057 to 0.066) in models adjusted for age, sex, race, SES, HCU, comorbidities, year of cancer diagnosis, SEER region, and histology. In subgroup analyses, this association was stronger for males, Non-Hispanic Black, American Indian Alaskan Native, and patients of lower SES. Conclusions: Approximately seven out of 10 patients with lung cancer have COPD, and late COPD diagnosis is common. Earlier COPD diagnosis is associated with diagnosis of lung cancer at earlier stages. These findings highlight the need for more structured screening of COPD among individuals at high risk for lung cancer.

3D plasmonic hexaplex paper sensor for label-free human saliva sensing and machine learning-assisted early-stage lung cancer screening

A label-free detection method for noninvasive biofluids enables rapid on-site disease screening and early-stage cancer diagnosis by analyzing metabolic alterations. Herein, we develop three-dimensional plasmonic hexaplex nanostructures coated on a paper substrate (3D-PHP). This flexible and highly absorptive 3D-PHP sensor is integrated with commercial saliva collection tube to create an efficient on-site sensing platform for lung cancer screening via surface-enhanced Raman scattering (SERS) measurement of human saliva. The multispike hexaplex-shaped gold nanostructure enhances contact with saliva viscosity, enabling effective sampling and SERS enhancement. Through testing patient salivary samples, the 3D-PHP sensor demonstrates successful lung cancer detection and diagnosis. A logistic regression-based machine learning model successfully classifies benign and malignant patients, exhibiting high clinical sensitivity and specificity. Additionally, important Raman peak positions related to different lung cancer stages are investigated, suggesting insights for early-stage cancer diagnosis. Integrating 3D-PHP senor with the conventional saliva collection tube platform is expected to offer promising practicality for rapid on-site disease screening and diagnosis, and significant advancements in cancer detection and patient care.