Detection Of Early-stage Cancer Research Articles

Serum proteomics profiling identifies a preliminary signature for the diagnosis of early-stage lung cancer.

Lung cancer is the most common cause of death from cancer worldwide, largely due to late diagnosis. Thus, there is an urgent need to develop new approaches to improve the detection of early-stage lung cancer, which would greatly improve patient survival. The quantitative protein expression profiles of microvesicles isolated from the sera from 46 lung cancer patients and 41 high-risk non-cancer subjects were obtained using a mass spectrometry method based on a peptide library matching approach. We identified 33 differentially expressed proteins that allow discriminating the two groups. We also built a machine learning model based on serum protein expression profiles that can correctly classify the majority of lung cancer cases and that highlighted a decrease in the levels of Arysulfatase A (ARSA) as the most discriminating factor found in tumors. Our study identified a preliminary, non-invasive protein signature able to discriminate with high specificity and selectivity early-stage lung cancer patients from high-risk healthy subjects. These results provide the basis for future validation studies for the development of a non-invasive diagnostic tool for lung cancer.

Antimonene-Coated Uniform-Waist Tapered Fiber Optic Surface Plasmon Resonance Biosensor for the Detection of Cancerous Cells: Design and Optimization.

For early-stage cancer detection, a novel design of graphene-antimonene-coated uniform-waist tapered fiber optic surface plasmon resonance (SPR) biosensor is demonstrated. The proposed optical biosensor outperforms over a wide range of refractive index (RI) variations including biological solutions and is designed to detect various cancerous cells in the human body whose RIs are in the range of 1.36-1.4. Here, antimonene is used to enhance the performance of the designed SPR sensor for sensing cancer analytes because of its high binding energy toward adsorption of biomolecules and large active surface area. The design and analysis of the sensor are done with the help of a transfer matrix method-based simulation platform, and the effect of the taper ratio is also studied. The performance of the proposed SPR biosensor is evaluated with performance parameters such as sensitivity, full width at half maximum, detection accuracy (DA), figure of merit (FOM), and limit of detection (LOD). The numerical results show that the designed sensor is able to provide a sensitivity of 7.3465, 10.9250, 11.8914, and 15.2414 μm/RIU, respectively, for sensing skin, cervical, blood, and adrenal gland cancer with a maximum FOM of 131.1525 RIU-1, DA of 14.2126 μm-1, and LOD of 7.2 × 10-5 RIU. Based on the derived results, the authors believe that the designed SPR sensor could practically find its potential applications in the field of medical science for the early-stage diagnosis of cancer and hence, opens a new window in the field of biosensing.

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

BackgroundLow-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases.MethodsSingle cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer.ResultsAutomated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83–0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89–0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71–0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state.ConclusionCyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis.Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018

Assessment of Liquid Biopsy in Primary Cutaneous Diffuse Large B-Cell Lymphoma–Leg Type

Liquid biopsy assessment has been first established in solid tumors, allowing the detection of either circulating tumor cells, cell-free vesicles and/or circulating tumor cell-free DNA (ctDNA), in the plasma or other biological fluids. In systemic diffuse large B-cell lymphomas (DLBCL), ctDNA is detectable, reflecting tumor mutational status, and may predict clinical outcome ( Bohers et al., 2018 Bohers E, Viailly P-J, Becker S, Marchand V, Ruminy P, Maingonnat C, et al. Non-Invasive Monitoring of Diffuse Large B-Cell Lymphoma by Cell-Free DNA High-Throughput Targeted Sequencing: Analysis of a Prospective Cohort. Blood Cancer J. 2018;1;8(8):74. Google Scholar ). In addition, dynamics of ctDNA correlates with clinical response ( Kurtz et al., 2019 Kurtz D.M. Esfahani M.S. Scherer F. Soo J. Jin M.C. Liu C.L. et al. Dynamic risk profiling using serial tumor biomarkers for personalized outcome Prediction. Cell. 2019; 178: 699-713.e19 Abstract Full Text Full Text PDF PubMed Scopus (97) Google Scholar ; Deng et al., 2020 Deng Q, G Han, N Puebla-Osorio, M Chun John Ma, P Strati, B Chasen, et al. Characteristics of Anti-CD19 CAR T Cell Infusion Products Associated with Efficacy and Toxicity in Patients with Large B Cell Lymphomas. Nat Med. 2020;26 (12): 1878-1887. Google Scholar ; Kurtz et al. 2018). Primary cutaneous diffuse large B-cell lymphoma-leg type (PCDLBCL-LT) is an aggressive cutaneous lymphoma, presenting as rapid progressive tumor(s) in elderly ( Willemze et al., 2019 Willemze R. Cerroni L. Kempf W. Berti E. Facchetti F. Swerdlow S.H. Jaffe E.S. The 2018 update of the WHO-EORTC classification for primary cutaneous lymphomas. Blood. 2019; 133: 1703-1714 Crossref PubMed Scopus (535) Google Scholar ). Mutational landscape of PCDLBCL-LT showed Activated B-cell-like DLBCL canonical mutations, ( Mareschal et al., 2017 Mareschal S. Pham-Ledard A. Viailly P.J. Dubois S. Bertrand P. Maingonnat C. et al. Identification of Somatic Mutations in Primary Cutaneous Diffuse Large B-Cell Lymphoma, Leg Type by Massive Parallel Sequencing. J Invest Dermatol. 2017; 137: 1984-1994 Abstract Full Text Full Text PDF PubMed Scopus (71) Google Scholar ; Zhou et al. 2018) especially with high prevalence of MYD88L265P (70%) and CD79B (40%) mutations that allows its classification in MCD or C5 subgroup among DLBCL ( Schmitz et al., 2018 Schmitz R. Wright G.W. Huang D.W. Johnson C.A. Phelan J.D. Wang J.Q. et al. Genetics and Pathogenesis of Diffuse Large B-Cell Lymphoma. N Eng J Med. 2018; 378: 1396-1407 Crossref PubMed Scopus (936) Google Scholar ) ( Chapuy et al., 2018 Chapuy B, Stewart C, Dunford AJ, Kim J, Kamburov A, Redd RA, et al. 2018. Molecular Subtypes of Diffuse Large B-cell Lymphoma are Associated with Distinct Pathogenic Mechanisms and Outcomes ». Nat Med. 2018;24(5):679-690. Google Scholar ). Genetic analysis on primary tumors may be limited by tumor site, sample size but also by spatial and temporal mutational heterogeneity (Condoluci et Rossi 2019; Phallen et al., 2017 Phallen J, Sausen M, Adleff V, Leal A, Hruban C, White J, et alDirect detection of early-stage cancers using circulating tumor DNA. Sci Transl Med. 2017;16;9(403):eaan2415. Google Scholar ). Droplet digital PCR (ddPCR) and its increased sensitivity of 0.01% (Diaz et Bardelli 2014) may detect infraclinical systemic involvement and appears appropriate for single hot-spot mutations detection such as MYD88L265P. Next Generation Sequencing (NGS) may be more appropriate to capture the mutational landscape of ctDNA ( Galimberti et al., 2019 Galimberti S. Genuardi E. Mazziotta F. Iovino L. Morabito F. Grassi S. et al. The Minimal Residual Disease in Non-Hodgkin’s Lymphomas: From the Laboratory to the Clinical Practice. Front Oncol. 2019; 26: 528 Crossref Scopus (14) Google Scholar ).

Plasma extracellular vesicle phenotyping for the differentiation of early-stage lung cancer and benign lung diseases.

The development of a minimally invasive technique for early-stage lung cancer detection is crucial to reducing mortality. Phenotyping of tumor-associated extracellular vesicles (EVs) has the potential for early-stage lung cancer detection, yet remains challenging due to the lack of sensitive, integrated techniques that can accurately detect rare tumor-associated EV populations in blood. Here, we integrated gold core-silver shell nanoparticles and nanoscopic mixing in a microfluidic assay for sensitive phenotypic analysis of EVs directly in plasma without EV pre-isolation. The assay enabled multiplex detection of lung cancer-associated markers PTX3 and THBS1 and canonical EV marker CD63 by surface-enhanced Raman spectroscopy, providing a squared correlation coefficient of 0.97 in the range of 103-107 EVs mL-1 and a limit of detection of 19 EVs mL-1. Significantly, our machine learning-based nanostrategy provided 92.3% sensitivity and 100% specificity in differentiating early-stage lung cancer from benign lung diseases, superior to the CT scan-based lung cancer diagnosis (92.3% sensitivity and 71.4% specificity). Overall, our integrated nanostrategy achieved an AUC value of 0.978 in differentiating between early-stage lung cancer patients (n = 28) and controls consisting of patients with benign lung diseases (n = 23) and healthy controls (n = 26), which showed remarkable diagnostic performance and great clinical potential for detecting the early occurrence of lung cancer.

Abstract IA023: Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers

Abstract Background: A multi-analyte blood test has the potential to maximize performance for early detection across different cancer stages and types. Improvements in early-stage cancer detection might be achieved using multi-component tests with high sensitivities and specificities. We recently performed a large feasibility study to assess the performance of 4 biomarkers (aneuploidy, methylation, mutation, and protein) for the detection of cancers from up to 15 organ sites. Specifically, a training and validation set was tested for 3 biomarkers (aneuploidy, methylation, and protein) and the performance was subsequently confirmed in an independent testing set. Methods: We have now further improved the performance of a 4-marker cancer detection blood test by fine-tuning the respective marker calling models and thresholds, exploring prostate-specific antigen (PSA) for prostate cancer detection, and developing an overarching Machine Learning (ML) cancer classifier. To improve the mutation detection, we tested (in triplicate) 200 plasma and buffy samples from young, non-cancer subjects and mutant DNA from cell lines to develop an ML-based mutation calling algorithm. This caller was validated on 186 samples and tested on an independent set of 1388 cancer and non-cancer samples. The calling of cancer-associated DNA methylation events was refined by performing training, validation, and testing across different studies. We also explored models for methylation detection based solely on distribution of methylation signal observed in non-cancer samples. Free and total PSA were investigated as markers for prostate cancer detection by including clinically relevant Gleason scores in the development of the protein-based cancer calling algorithm. Results: In the previous analysis the combination of mutation, aneuploidy, methylation, and protein biomarkers resulted in an overall sensitivity of 61.0% (95% CI: 56.9%-65.0) at a specificity of 98.2% (95% CI: 97.1 – 99.4%). We will present the added performance benefit of ML-based mutation variant calling. PSA derived features were evaluated with the goal of increasing the detectability of high-grade prostate cancers while minimizing the detection of indolent cancers. Lastly, we compared the Boolean logic-based 4-biomarker combination algorithm used in the previous analysis with an ML-based cancer classifier. The results of the modeling, applied to the testing set, will be shared. Conclusions: In summary, improvements in cancer detection performance may be achieved by optimizing each biomarker calling algorithm as well as overarching cancer classifier. When combining these improvements, we believe that a single blood test will provide robust sensitivity for the detection of several cancer types, particularly for earlier-stage disease in real world settings. Citation Format: Vladimir Gianullin, Leonardo Hagmann, Kevin Arvai, Amira Djebbari, Christopher L. Nobles, Larson Hogstrom, Mael Manesse, Vuna Fa, Fanglei Zhuang, Xi Chen, Viatcheslav E. Katerov, Jorge Garces, Hatim T. Allawi, Abigail McElhinny, Frank Diehl, Gustavo C Cerqueira. Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr IA023.

Abstract P041: Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers

Abstract Background: A multi-analyte blood test has the potential to maximize performance for early detection across different cancer stages and types. Improvements in early-stage cancer detection might be achieved using multi-component tests with high sensitivities and specificities. We recently performed a large feasibility study to assess the performance of 4 biomarkers (aneuploidy, methylation, mutation, and protein) for the detection of cancers from up to 15 organ sites. Specifically, a training and validation set was tested for 3 biomarkers (aneuploidy, methylation, and protein) and the performance was subsequently confirmed in an independent testing set. Methods: We have now further improved the performance of a 4-marker cancer detection blood test by fine-tuning the respective marker calling models and thresholds, exploring prostate-specific antigen (PSA) for prostate cancer detection, and developing an overarching Machine Learning (ML) cancer classifier. To improve the mutation detection, we tested (in triplicate) 200 plasma and buffy samples from young, non-cancer subjects and mutant DNA from cell lines to develop an ML-based mutation calling algorithm. This caller was validated on 186 samples and tested on an independent set of 1388 cancer and non-cancer samples. The calling of cancer-associated DNA methylation events was refined by performing training, validation, and testing across different studies. We also explored models for methylation detection based solely on distribution of methylation signal observed in non-cancer samples. Free and total PSA were investigated as markers for prostate cancer detection by including clinically relevant Gleason scores in the development of the protein-based cancer calling algorithm. Results: In the previous analysis the combination of mutation, aneuploidy, methylation, and protein biomarkers resulted in an overall sensitivity of 61.0% (95% CI: 56.9%-65.0) at a specificity of 98.2% (95% CI: 97.1 – 99.4%). We will present the added performance benefit of ML-based mutation variant calling. PSA derived features were evaluated with the goal of increasing the detectability of high-grade prostate cancers while minimizing the detection of indolent cancers. Lastly, we compared the Boolean logic-based 4-biomarker combination algorithm used in the previous analysis with an ML-based cancer classifier. The results of the modeling, applied to the testing set, will be shared. Conclusions: In summary, improvements in cancer detection performance may be achieved by optimizing each biomarker calling algorithm as well as overarching cancer classifier. When combining these improvements, we believe that a single blood test will provide robust sensitivity for the detection of several cancer types, particularly for earlier-stage disease in real world settings. Citation Format: Vladimir Gianullin, Leonardo Hagmann, Kevin Arvai, Amira Djebbari, Christopher L. Nobles, Larson Hogstrom, Mael Manesse, Vuna Fa, Fanglei Zhuang, Xi Chen, Viatcheslav E. Katerov, Jorge Garces, Hatim T. Allawi, Abigail McElhinny, Frank Diehl, Gustavo C Cerqueira. Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P041.

The Approach to Performance of Quality Upper Endoscopy in Lynch Syndrome (QUELS)

Strong evidence demonstrates the protective benefit of frequent colonoscopy surveillance for colorectal cancer prevention in Lynch Syndrome (LS) and is endorsed by many guidelines. Until recently, the evidence supporting the utility of upper endoscopy [esophagogastroduodenoscopy (EGD)] for upper gastrointestinal (UGI) cancer surveillance was limited. Over the last 3 years, multiple studies have demonstrated that EGD surveillance in LS is associated with the detection of both precancerous lesions and early-stage UGI cancers. On the basis of the emerging favorable evidence derived from EGD surveillance programs, the 2022 National Comprehensive Cancer Network (NCCN) Guidelines for LS recommend UGI surveillance with EGD starting between age 30 and 40 years with repeat EGDs every 2 to 4 years, preferably in conjunction with colonoscopy, in all patients with a germline pathogenic variant (PV) in MLH1, MSH2, EPCAM, and MSH6 and, because of the lack of data, consideration in PMS2. Standardization of the approach to performing EGD surveillance in LS and reporting clinically actionable findings is requisite for both improving quality and understanding the cost efficiency and outcomes of patients undergoing EGD as a surveillance tool. Accordingly, the primary objective of this Quality of Upper Endoscopy in Lynch Syndrome (QUELS) statement is to articulate a framework for standardizing the approach to performing and reporting EGD findings in patients with LS by introducing emerging quality metrics. The recommendations presented herein were developed from available evidence and consensus-based expert opinion and provide a practical approach for clinicians applying EGD surveillance in accordance with the most recent and existing LS guidelines.

Cervical Cancer Screening with DNA-HPV Testing and Precancerous Lesions Detection: A Brazilian Population-based Demonstration Study.

To evaluate the rates of precancerous lesions, colposcopy referral, and positive predictive value (PPV) by age groups of a population-based screening with DNA-HPV testing. The present demonstration study compared 16,384 HPV tests performed in the first 30 months of the program with 19,992 women tested in the cytology screening. The colposcopy referral rate and PPV for CIN2+ and CIN3+ by age group and screening program were compared. The statistical analysis used the chi-squared test and odds ratio (OR) with 95% confidence interval (95%CI). The HPV tests were 3.26% positive for HPV16-HPV18 and 9.92% positive for 12 other HPVs with a 3.7 times higher colposcopy referral rate than the cytology program, which had 1.68% abnormalities. Human Papillomavirus testing detected 103 CIN2, 89 CIN3, and one AIS, compared with 24 CIN2 and 54 CIN3 detected by cytology (p < 0.0001). The age group between 25 and 29 years old screened by HPV testing had 2.4 to 3.0 times more positivity, 13.0% colposcopy referral, twice more than women aged 30 to 39 years old (7.7%; p < 0.0001), and detected 20 CIN3 and 3 early-stage cancer versus 9 CIN3 and no cancer by cytology screening (CIN3 OR= 2.10; 95%CI: 0.91-5.25; p = 0.043). The PPV of colposcopy for CIN2+ ranged from 29.5 to 41.0% in the HPV testing program. There was a significant increase in detections of cervix precancerous lesions in a short period of screening with HPV testing. In women < 30 years old, the HPV testing exhibited more positivity, high colposcopy referral rate, similar colposcopy PPV to older women, and more detection of HSIL and early-stage cervical cancer.

Abstract PR006: Screening under-served populations by mobile low dose computerized tomography (LDCT) scans results in stage shift with potential cures: Time for a change in health policy?

Abstract Background: More than 60% of under-served patients with lung cancer first present with advanced/metastatic disease. Randomized trials have shown that LDCT of chest reduces lung cancer mortality but under-served populations have been excluded from these studies because of financial, geographic, insurance and access problems. We have tested the first mobile LDCT units and demonstrated improved access. We report a shift to earlier stage at diagnosis and potential cures in a large under-served population that includes African American, Hispanic/LatinX and Native American subjects. Methods: We developed two coaches fitted with portable 32 slice low-dose CT scanners to screen uninsured and under-served heavy smokers for lung cancer in an IRB-approved program ("The Oncologist", 2019). All films were reviewed by a central panel (using LUNG RADS protocol). Medicare patients were excluded because their insurance covers LDCT lung screening (but it should be noted that the elderly population is at increased risk for lung cancer, leading to a negative case selection bias). Results: We initially screened 1200 uninsured/under-insured subjects, mean age 61 years (range 55-64), with average pack year history of 47.8 (range 30- &amp;gt;150); 61% male; 18% Black, 3% Hispanic/LatinX, &amp;lt;1% Native American; 78% rural. We found 97 pts with LUNG RADS 4 (very high risk) lesions, 30 lung cancers (2.5%), including 18 at stage I-III treated with curative intent (60%); &amp;gt;50% had intercurrent cardiovascular disease and COPD seen on LDCT. We have additionally screened 574 cases, including 51 Native Americans (in a structured assessment of one tribe). Of the Native Americans, 5 had LUNG RADS 3-4 (high risk) lesions, but no cancers have been identified to date. For the total series of 1774 subjects with age-related negative case selection bias, 42 lung cancers have been identified (2.4%), 26 of which were treated with curative intent (62%) via surgery, radiation, or chemo-radiation. Only one patient of 18 treated with curative intent in the first series of 30 lung cancers has relapsed to date (median follow up 3 years, with 5 without evidence of disease beyond 3.5 years). These data confirm a shift to detection of early-stage lung cancer in this large under-served population, with an apparent potential for cure. Conclusion: Mobile LDCT yields a higher screening rate for under-served patients than prior hallmark trials, with a consequent shift to early-stage detection of lung cancer, with sustained treatment-induced complete remissions beyond 4 years. This approach could be applied to improve national lung cancer survival in the under-served. Citation Format: Derek Raghavan, Darcy Doege, Mellisa Wheeler, Kia Dungan, John Doty III, Glenn Hickman, Kathryn Mileham, Daniel Carrizosa. Screening under-served populations by mobile low dose computerized tomography (LDCT) scans results in stage shift with potential cures: Time for a change in health policy? [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr PR006.

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network.

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

Aptamer-functionalized two-photon SiO2@GQDs hybrid-based signal amplification strategy for targeted cancer imaging.

Targeted imaging is playing an increasingly important role in the early detection and precise diagnosis of cancer. This need has motivated research into sensory nanomaterials that can be constructed into imaging agents to serve as biosensors. Graphene quantum dots (GQDs) as a valuable nanoprobe show great potential for use in two-photon biological imaging. However, most as-prepared GQDs exhibit a low two-photon absorption cross-section, narrow spectral coverage, and "one-to-one" signal conversion mode, which greatly hamper their wide application in sensitive early-stage cancer detection. Herein, a versatile strategy has been employed to fabricate an aptamer Sgc8c-functionalized hybrid as a proof-of-concept of the signal amplification strategy for targeted cancer imaging. In this study, GQDs with two-photon imaging performance, and silica nanoparticles (SiO2 NPs) as nanocarriers to provide amplified recognition events by high loading of GQD signal tags, were adopted to construct a two-photon hybrid-based signal amplification strategy. Thus, the obtained hybrid (denoted SiO2@GQDs) enabled extremely strong fluorescence with a quantum yield up to 0.49, excellent photostability and biocompatibility, and enhanced bright two-photon fluorescence up to 2.7 times that of bare GQDs (excitation at 760 nm; emission at 512 nm). Moreover, further modification with aptamer Sgc8c showed little disruption to the structure of the SiO2@GQDs-hybrid and the corresponding two-photon emission. Hence, SiO2@GQDs-Sgc8c showed specific responses to target cells. Moreover, it could be used as a signal-amplifying two-photon nanoprobe for targeted cancer imaging with high specificity and great efficiency, which exhibits a distinct green fluorescence compared to that of GQDs-Sgc8c or SiO2@GQDs. This signal amplification strategy holds great potential for the accurate early diagnosis of tumors and offers new tools for the detection a wide variety of analytes in clinical application.

Additional Diffusion-Weighted Imaging with Background Body Signal Suppression (DWIBS) Improves Pre-Therapeutical Detection of Early-Stage (pT1a) Glottic Cancer: A Feasibility and Interobserver Reliability Study.

(1) Background: Early-stage glottic cancer is easily missed on magnetic resonance imaging (MRI). Diffusion-weighted imaging (DWI) may improve diagnostic accuracy. Therefore, our aim was to assess the value of adding diffusion-weighted imaging with background body signal suppression (DWIBS) to pre-therapeutic MRI staging. (2) Methods: Two radiologists with 8 and 13 years of experience, blinded to each other's findings, initially interpreted only standard MRI, later DWIBS alone, and afterward, standard MRI + DWIBS in 41 patients with histopathologically proven pT1a laryngeal cancer of the glottis. (3) Results: Detectability rates with standard MRI, DWIBS only, and standard MRI + DWIBS were 68-71%, 63-66%, and 73-76%, respectively. Moreover, interobserver reliability was calculated as good (κ = 0.712), very good (κ = 0.84), and good (κ = 0.69) for standard MRI, DWIBS only, and standard MRI + DWIBS, respectively. (4) Conclusions: Standard MRI, DWIBS alone, and standard MRI + DWIBS showed an encouraging detection rate, as well as distinct interobserver reliability in the diagnosis of early-stage laryngeal cancer when compared to the definitive histopathologic report.

An Overview on Electrochemical Approaches for the Detection of Alpha-Fetoprotein

Cancer biomarkers have a remarkable role for not only early-stage detection of cancer but also in the monitoring therapy efficacy. Alpha-fetoprotein (AFP) is one of the biomarkers which is highly expressed in hepatocellular carcinoma (HCC) patients. Therefore, a high level of AFP can be considered for early diagnosis of HCC. Electrochemical assays can be used as the miniaturized biosensors for real-time detection of the analytes which are mostly categorized into three groups for AFP detection including immunosensors, aptasensors, and molecularly imprinted polymer-based sensors. All of these techniques provide beneficial detection features of fast and cost-effective compared with other analytical methods. This review describes the most recent electrochemical platforms for AFP detection to provide a perspective for future studies.

Postoperative lung cancer surveillance: the highs and lows of computerized tomographic scanning.

Radiation dose reduction has been a topic of great interest in the last decade. In this study, Nakada and colleagues address a very important contributor of radiation exposure in today’s world, i.e. lung cancer surveillance [1]. Since the USPSTF’s recommendations on lung cancer screening, there has been an increase in the detection and treatment of early-stage lung cancers [2]. With the recent screening guidelines including younger patients, we are bound to see an increase in the pool of patients that are now exposed to intensive surveillance with computerized tomographic (CT) scans after lung cancer treatment. Nakada et al. make a case here to balance our surveillance strategy with the radiation exposure that we commit these patients to. They present a retrospective study of 416 patients who underwent lobectomy or segmentectomy for stage I non-small-cell lung cancer (NSCLC) and were surveilled with low-dose CT (LDCT) or conventional CT with a median follow-up time of 57 months. In the propensity score-matched analysis, they did not find a significant difference in the recurrence-free survival or the overall survival between the 2 groups.

Serum protein profiling of lung, pancreatic, and colorectal cancers reveals alcohol consumption-mediated disruptions in early-stage cancer detection

While the link between serum proteins and cancer has been studied in an effort to enable early-stage cancer detection, factors that might perturb this link has been poorly understood. To ask this question, we performed serum protein profiling on a prospective cohort of 601 individuals with or without lung, pancreatic, or colorectal cancers and identified ten distinct serum protein signatures with distinct link to the patient metadata. Importantly, we discovered that a positive history of alcohol consumption is a major factor that diminishes the sensitivity of serum protein-mediated liquid biopsy in early-stage malignancies, resulting in a 44% decline in the sensitivity of detecting American Joint Committee on Cancer (AJCC) stage I malignancies. Our data provide evidence that patient lifestyle can affect the sensitivity of liquid biopsy and suggest the potential need for abstinence from alcohol before measurement during serum protein-based cancer screening.

Evaluation of optical features of fibronectin fibrils by backscattering polarization imaging

Polarization imaging provides an effective and convenient approach for examining the structural and optical features of scattering media. The present study utilizes a backscattering polarization imaging system to evaluate and compare the polarization features of three different fibronectin (FN) samples: (1) fibronectin-free in phosphate-buffered saline (PBS) solution, (2) fibronectin protein (PFN), and (3) fibrillated fibronectin (FFN). It is shown that the three samples can be qualitatively discriminated via a simple naked-eye observation of the Mueller matrix imaging results. Furthermore, a more quantitative evaluation of the polarization properties of the three samples can be obtained from an inspection of the Mueller Matrix Transformation (MMT) parameters and Frequency Distribution Histogram (FDH) statistics for the pixel intensities of the Mueller matrix elements. The results show that the PFN and PBS samples have isotropic properties, while the FFN sample has anisotropic properties. Overall, the results confirm that the proposed backscattering polarization imaging system provides a feasible means of differentiating between different fibronectin-containing biological samples. It thus has significant potential for various biomedical diagnosis applications, including early-stage cancer detection.

Prediction of tissue-of-origin of early stage cancers using serum miRNomes.

Noninvasive detection of early stage cancers with accurate prediction of tumor tissue-of-origin could improve patient prognosis. Because miRNA profiles differ between organs, circulating miRNomics represent a promising method for early detection of cancers, but this has not been shown conclusively. A serum miRNA profile (miRNomes)-based classifier was evaluated for its ability to discriminate cancer types using advanced machine learning. The training set comprised 7931 serum samples from patients with 13 types of solid cancers and 5013 noncancer samples. The validation set consisted of 1990 cancer and 1256 noncancer samples. The contribution of each miRNA to the cancer-type classification was evaluated, and those with a high contribution were identified. Cancer type was predicted with an accuracy of 0.88 (95% confidence interval [CI] = 0.87 to 0.90) in all stages and an accuracy of 0.90 (95% CI = 0.88 to 0.91) in resectable stages (stages 0-II). The F1 score for the discrimination of the 13 cancer types was 0.93. Optimal classification performance was achieved with at least 100 miRNAs that contributed the strongest to accurate prediction of cancer type. Assessment of tissue expression patterns of these miRNAs suggested that miRNAs secreted from the tumor environment could be used to establish cancer type-specific serum miRNomes. This study demonstrates that large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system. Further investigations of the regulating mechanisms of the miRNAs that contributed strongly to accurate prediction of cancer type could pave the way for the clinical use of circulating miRNA diagnostics.

Clinical Value of Growth Differentiation Factor 15 Detection in the Diagnosis of Early Liver Cancer Based on Data Mining.

The incidence of liver cancer is increasing year by year, and how to effectively diagnose early-stage liver cancer and improve the survival rate of liver cancer patients has become one of the current research topics of concern. Aiming at this problem, it is of great significance for the diagnosis of early liver cancer. With the in-depth research on the diagnosis of early-stage liver cancer, the research on growth differentiation factor 15 is gradually carried out, and its performance advantages are of great significance to solve the problem of detection and diagnosis of early-stage liver cancer. This study can improve the accuracy of early diagnosis of liver cancer. The purpose of this paper is to study the application of data mining in the study of clinical value of growth and differentiation factor 15 detection and diagnosis of early liver cancer. In this paper, the data mining algorithm is analyzed, the performance of the algorithm is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results showed that the expression level of GDF-15 was significant in early primary liver cancer (tumor diameter <2.5 cm). Different from normal liver tissue (P < 0.01), there was a significant difference (P < 0.01) compared with adjacent tissue (P < 0.01). Serum GDF-15 can be used as a tumor marker for predicting early stage liver cancer. The high expression of GDF-15 in early stage liver cancer is an independent risk factor affecting the prognosis of liver cancer patients.

LCSCNet: A multi-level approach for lung cancer stage classification using 3D dense convolutional neural networks with concurrent squeeze-and-excitation module

Lung cancer, the deadliest disease worldwide, poses a massive threat to humankind. Various researchers have designed Computer-Aided-Diagnosis systems for the early-stage detection of lung cancer. However, patients are primarily diagnosed in advanced stages when treatment becomes complicated and dependent on multiple factors like size, nature, location of the tumor, and proper cancer staging. TNM (Tumor, Node, and Metastasis) staging provides all this information. This study aims to develop a novel and efficient approach to classify lung cancer stages based on TNM standards. We propose a multi-level 3D deep convolutional neural network, LCSCNet (Lung Cancer Stage Classification Network). The proposed network architecture consists of three similar classifier networks to classify three labels, T, N, and M-labels. First, we pre-process the data, in which the CT images are augmented, and the label files are processed to get the corresponding TNM labels. For the classification network, we implement a dense convolutional neural network with a concurrent squeeze & excitation module and asymmetric convolutions for classifying each label separately. The overall stage is determined by combining all three labels. The concurrent squeeze & excitation module helps the network focus on the essential information of the image, due to which the classification performance is enhanced. The asymmetric convolutions are introduced to reduce the computation complexity of the network. Two publicly available datasets are used for this study. We achieved average accuracies of 96.23% for T-Stage, 97.63% for N-Stage, and 96.92% for M-Stage classification. Furthermore, an overall stage classification accuracy of 97% is achieved.