Optimize Cancer Detection Research Articles

Abstract LB100: Performance of a multi-analyte, multi-cancer early detection (MCED) blood test in a prospectively-collected cohort

Abstract Background: A multi-analyte blood test has potential for robust sensitivity in detecting a broad range of cancer types and stages. Previously, using retrospectively collected samples, we trained and independently assessed in a case-control study the performance of various biomarker classes for the detection of cancers (Douville, Annals of Oncology,2022:pS575). The aim of this study was to further assess two of the four original biomarker classes (methylation and protein) using samples from a large, multi-center, prospectively collected study: Ascertaining Serial Cancer patients to Enable New Diagnostic 2 (ASCEND 2). Methods: The study included 6,314 samples (1,426 cancer and 4,888 non-cancer) collected in LBgard® tubes. The training cohort included 3,026 samples (654 cancers and 2,372 non-cancer controls) and the test set included 3,288 samples (772 cancers and 2,516 non-cancer controls). By measuring biomarkers that capture shared cancer signals, the test is designed to detect a broad range of cancer types. In this analysis, cancer stages were well-represented across 21 solid and hematologic tumor organ sites that collectively represent >85% of incident cancers. We combined DNA methylation and protein biomarkers in a single overarching classifier. Machine learning models were calibrated using stratified cross-validation to reach optimal cancer detection at a combined specificity >99.0%. Results: The specificity threshold identified in training resulted in an overall test set sensitivity of 49.9% and 99.0% specificity, with sensitivities of 23.6% (n=364), 68.6% (n=191), 84.6% (n=182), and 40.0% (n=35) for stages I/II, III, IV, and unknown, respectively. We will also report the cross-validation results of the multi-analyte classifiers in this newly enrolled case-control study. Conclusions: In ASCEND2, a large prospectively collected case-control study that included >85% of cancer burden, new training data and updated models demonstrated robust performance of methylation and protein biomarkers. These results demonstrate reproducibility of the multi-biomarker class approach to cancer detection in the ASCEND 2 cohort and represent an evolution in cancer classifier design. Citation Format: Vladimir Gainullin, Hee Jung Hwang, Larson Hogstrom, Kevin Arvai, Amira Best, Melissa Gray, Madhav Kumar, Mael Manesse, Xi Chen, Philip Uren, Amin Mazloom, Gustavo Cerqueira, Jorge Garces, Tomasz M. Beer, Abigail Mcelhinny, Frank Diehl. Performance of a multi-analyte, multi-cancer early detection (MCED) blood test in a prospectively-collected cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB100.

Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model.

Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process.

Future of Breast Radiology.

The landscape of breast imaging has transformed significantly since mammography's introduction in the 1960s, accelerated by ultrasound and imageguided biopsies in the 1990s. The emergence of magnetic resonance imaging (MRI) in the 2000s added a valuable dimension to advanced imaging. Multimodality and multiparametric imaging have firmly established breast radiology's pivotal role in managing breast disorders. A shift from conventional to digital radiology emerged in the late 20th and early 21st centuries, enabling advanced techniques like digital breast tomosynthesis, contrast-enhanced mammography, and artificial intelligence (AI) integration. AI's impending integration into breast radiology may enhance diagnostics and workflows. It involves computer-aided diagnosis (CAD) algorithms, workflow support algorithms, and data processing algorithms. CAD systems, developed since the 1980s, optimize cancer detection rates by addressing false positives and negatives. Radiologists' roles will evolve into specialized clinicians collaborating with AI for efficient patient care and utilizing advanced techniques with multiparametric imaging and radiomics. Wearable technologies, non-contrast MRI, and innovative modalities like photoacoustic imaging show potential to enhance diagnostics. Imaging-guided therapy, notably cryotherapy, and theranostics, gains traction. Theranostics, integrating therapy and diagnostics, holds potential for precise treatment. Advanced imaging, AI, and novel therapies will revolutionize breast radiology, offering refined diagnostics and personalized treatments. Personalized screening, AI's role, and imaging-guided therapies will shape the future of breast radiology.

Federated auto-encoder and XGBoost schemes for multi-omics cancer detection in distributed fog computing paradigm

The digital healthcare paradigm has significantly improved based on distributed fog and cloud networks for cancer detection with multiple classes in recent years. The paradigm allows the collecting and training of cancer data on various computing nodes to make optimal cancer detection with their classes. In paradigm, multi-omics approaches (such as RNA, miRNA, and methylation) and machine learning techniques have achieved remarkable results in predicting cancer with different features. However, the digital healthcare paradigm for cancer detection concerning infrastructure still faces challenges related to security, execution delay, and improving the accuracy of cancer prediction in existing studies. These limitations affect the overall cancer detection results with more accuracy. In this paper, we are handling the research mentioned above limitations. We present a new paradigm for cancer detection with more accuracy, less processing delay, and more security based on fog cloud heterogeneous computing nodes. We present the Multi-Cancer Multi-Omics Clinical Dataset Laboratories (MCMOCL) Schemes to predict multi-cancers with multiple classes and consist of federated learning, auto-encoder, and XGBoost methods. The main objective of this study is to improve accuracy, reduce execution delay, and improve the security among heterogeneous cancer clinics in the architecture. Simulation results show that MCMOCL outperformed all existing machine models in terms of accuracy by 98% , processing delay by 61%, and security for the multi-classes types of cancers in heterogeneous fog cloud paradigm.

Optimal lung cancer detection based on CNN optimized and improved Snake optimization algorithm

Lung cancer is an ailment that lessens the patient's capability to breathe because of the excessive spread of lung tissue cells. The cancer spreads after genetic damage to DNA. This genetic damage results in the excessive distribution and proliferation of cells, which ultimately creates a mass called a tumor. Using automatic machine vision techniques is an effective method for lung cancer detection. A novel lung cancer detection methodology for the automatic identification of lung cancer in CT images is proposed in this work This technique is developed by convolutional neural networks (CNNs). Here, the lung cancer images are first preprocessed before being included in the designed CNN. To provide optimal CNN design, a novel modified version of Snake Optimizer is designed and applied to the CNN structure. The proposed model is then tested against the “IQ-OTH/NCCD-Lung Cancer Dataset”. This strategy is also validated by comparing it with some different published methods in the literature. The findings demonstrated that the offered strategy outperformed the others.

Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search.

Recently, pre-trained deep learning (DL) models have been employed to tackle and enhance the performance on many tasks such as skin cancer detection instead of training models from scratch. However, the existing systems are unable to attain substantial levels of accuracy. Therefore, we propose, in this paper, a robust skin cancer detection framework for to improve the accuracy by extracting and learning relevant image representations using a MobileNetV3 architecture. Thereafter, the extracted features are used as input to a modified Hunger Games Search (HGS) based on Particle Swarm Optimization (PSO) and Dynamic-Opposite Learning (DOLHGS). This modification is used as a novel feature selection to alloacte the most relevant feature to maximize the model's performance. For evaluation of the efficiency of the developed DOLHGS, the ISIC-2016 dataset and the PH2 dataset were employed, including two and three categories, respectively. The proposed model has accuracy 88.19% on the ISIC-2016 dataset and 96.43% on PH2. Based on the experimental results, the proposed approach showed more accurate and efficient performance in skin cancer detection than other well-known and popular algorithms in terms of classification accuracy and optimized features.

RETRACTED: Optimal skin cancer detection by a combined ENN and Fractional Order Coot Optimization Algorithm.

RETRACTED: Optimal skin cancer detection by a combined ENN and Fractional Order Coot Optimization Algorithm.

RETRACTED: Optimal skin cancer detection by a combined ENN and Fractional Order Coot Optimization Algorithm.

In recent years, skin cancer has been recognized as the most dangerous and common type of cancer in humans. There are different types of skin cancer that can be diagnosed early. Providing a method that facilitates the diagnosis of this cancer in the early stages is very useful and valuable. This study proposes a new algorithm to efficient diagnosis of the skin cancer by proper extracting and selection of the features from the image (gradient, texture, and geometric features) and classifying these features to diagnose the cancerous cases. The classification step of the methodology based on Elman neural network. Due to the effectiveness of the metaheuristic algorithms in optimal solving the problems in the last decade, using these kinds of optimization techniques is useful for the diagnosis purpose. Therefore, both parts of feature selection and classification stages are optimized by an improved metaheuristic algorithm, called Fractional Order Coot Optimization Algorithm (FO-COA) which is designed in this study. Final designed model is then performed to the SIIM-ISIC Melanoma dataset and its achievements have been validated with several newest approaches to show the method higher efficiency.

The "Sweet Spot" Revisited: Optimal Recall Rates for Cancer Detection With 2D and 3D Digital Screening Mammography in the Metro Chicago Breast Cancer Registry.

OBJECTIVE. One central question pertaining to mammography quality relates to discerning the optimal recall rate to maximize cancer detection while minimizing unnecessary downstream diagnostic imaging and breast biopsies. We examined the trade-offs for higher recall rates in terms of biopsy recommendations and cancer detection in a single large health care organization. MATERIALS AND METHODS. We included 2D analog, 2D digital, and 3D digital (tomosynthesis) screening mammography examinations among women 40-79 years old performed between January 1, 2005, and December 31, 2017, with cancer follow-up through 2018. There were 36, 67, and 38 radiologists who read at least 1000 2D analog examinations, 2D digital examinations, and 3D tomosynthesis examinations, respectively, who were included in these analyses. Using logistic regression with marginal standardization, we estimated radiologist-specific mean recall (abnormal interpretations/1000 mammograms), biopsy recommendation, cancer detection (screening-detected in situ and invasive cancers/1000 mammograms), and minimally invasive cancer detection rates while adjusting for differences in patient characteristics. RESULTS. Among 1,060,655 screening mammograms, the mean recall rate was 10.7%, the cancer detection rate was 4.0/1000 mammograms, and the biopsy recommendation rate was 1.60%. Recall rates between 7% and 9% appeared to maximize cancer detection while minimizing unnecessary biopsies. CONCLUSION. The results of this investigation are in contrast to those of a recent study suggesting appropriateness of higher recall rates. The "sweet spot" for optimal cancer detection appears to be in the recall rate range of 7-9% for both 2D digital mammography and 3D tomosynthesis. Too many women are being called back for diagnostic imaging, and new benchmarks could be set to reduce this burden.

Cancer and scleroderma: recent insights.

As survival in systemic sclerosis (SSc) improves, research interest has shifted to the leading cause of non-SSc-related death, namely cancer, which accounts for over a third of non-SSc-related deaths. This review will provide an overview of the recent insights into the evolving relationship between SSc and cancer. Recent studies confirm the increased risk of cancer in SSc compared with the general population (standardized incidence ratio 1.9-2.2) in particular the risk of breast, lung and skin cancer. This increased cancer risk, particularly occurring in close proximity to SSc onset, raises the novel concept of autoimmunity occurring as a direct immune response to the cancerous cells. We highlight the important role that SSc-specific autoantibodies may have in identifying these at-risk patients, prognostication and triaging those who may require tight surveillance and further cancer screening. The knowledge will allow the development of future prospective studies evaluating clinically relevant and targeted cancer screening strategies for newly diagnosed SSc patients to optimize cancer detection while minimizing harms and costs from overscreening.

Comparative Analysis of Urine Fractions for Optimal Bladder Cancer Detection Using DNA Methylation Markers.

DNA methylation analysis of full void urine and urine pellet seems promising for bladder cancer (BC) detection and surveillance. Urinary cell-free DNA from urine supernatant is now gaining interest for other molecular tests in BC. This study aims to evaluate which urine fraction is preferred for BC diagnosis using methylation markers: full void urine, urine pellet or supernatant. Methylation levels of nine markers were determined in the three urine fractions and correlated with their respective tumor tissues in BC patients and compared to controls. For all markers and marker panel GHSR/MAL, diagnostic performance was determined by calculating the area under the curve (AUC) of the respective receiver operating characteristic curves. For most of the markers, there was a significant correlation between the methylation levels in each of the urine fractions and the matched tumor tissues. Urine pellet was the most representative fraction. Generally, AUCs for BC diagnosis were comparable among the fractions. The highest AUC was obtained for GHSR/MAL in urine pellet: AUC 0.87 (95% confidence interval: 0.73–1.00), corresponding to a sensitivity of 78.6% and a specificity of 91.7%. Our results demonstrate that cellular and cell-free DNA in urine can be used for BC diagnosis by urinary methylation analysis. Based on our comparative analysis and for practical reasons, we recommend the use of urine pellet.

Cost-effectiveness of Common Diagnostic Approaches for Evaluation of Asymptomatic Microscopic Hematuria

Asymptomatic microscopic hematuria (AMH) is highly prevalent and may signal occult genitourinary (GU) malignant abnormality. Common diagnostic approaches differ in their costs and effectiveness in detecting cancer. Given the low prevalence of GU malignant abnormality among patients with AMH, it is important to quantify the cost implications of detecting cancer for each approach. To estimate the effectiveness, costs, and incremental cost per cancer detected (ICCD) for 4 common diagnostic approaches evaluating AMH. A decision-analytic model-based cost-effectiveness analysis using inputs from the medical literature. PubMed searches were performed to identify relevant literature for all key model inputs, each of which was derived from the clinical study with the most robust data and greatest applicability. Analysis included adult patients with AMH on routine urinalysis with subgroups of high-risk patients (males, smokers, age ≥50 years) seen in the primary care or urologic referral setting. Four diagnostic approaches were evaluated relative to the reference case of no evaluation: (1) computed tomography (CT) alone; (2) cystoscopy alone; (3) CT and cystoscopy combined; and (4) renal ultrasound and cystoscopy combined. At termination of the diagnostic period, cancers detected, costs (payer perspective), and ICCD per 10 000 patients evaluated for AMH. Of the 4 diagnostic approaches analyzed, CT alone was dominated by all other strategies, detecting 221 cancers at a cost of $9 300 000. Ultrasound and cystoscopy detected 245 cancers and was most cost-effective with an ICCD of $53 810. Replacing ultrasound with CT detected just 1 additional cancer at an ICCD of $6 480 484. Ultrasound and cystoscopy remained the most cost-effective approach in subgroup analysis. The model was not sensitive to any inputs within the proposed ranges. Using probabilistic sensitivity analysis, ultrasound and cystoscopy was the dominant strategy in 100% of simulations. The combination of renal ultrasound and cystoscopy is the most cost-effective among 4 diagnostic approaches for the initial evaluation of AMH. The use of ultrasound in lieu of CT as the first-line diagnostic strategy will optimize cancer detection and reduce costs associated with evaluation of AMH. Given our findings, we need to critically evaluate the appropriateness of our current clinical practices, and potentially alter our guidelines to reflect the most effective screening strategies for patients with AMH.

MP33-20 THE SMARTTARGET BIOPSY TRIAL: A PROSPECTIVE PAIRED BLINDED TRIAL WITH RANDOMISATION TO COMPARE VISUAL-ESTIMATION AND IMAGE-FUSION TARGETED PROSTATE BIOPSIES

You have accessJournal of UrologyProstate Cancer: Detection & Screening III1 Apr 2017MP33-20 THE SMARTTARGET BIOPSY TRIAL: A PROSPECTIVE PAIRED BLINDED TRIAL WITH RANDOMISATION TO COMPARE VISUAL-ESTIMATION AND IMAGE-FUSION TARGETED PROSTATE BIOPSIES Ian Donaldson, Sami Hamid, Dean Barratt, Yipeng Hu, Rachel Rodell, Barbara Villarini, Ester Bonmati, Paul Martin, David Hawkes, Neil McCartan, Ingrid Potyka, Norman Williams, Chris Brew-Graves, Caroline Moore, Mark Emberton, and Hashim Ahmed Ian DonaldsonIan Donaldson More articles by this author , Sami HamidSami Hamid More articles by this author , Dean BarrattDean Barratt More articles by this author , Yipeng HuYipeng Hu More articles by this author , Rachel RodellRachel Rodell More articles by this author , Barbara VillariniBarbara Villarini More articles by this author , Ester BonmatiEster Bonmati More articles by this author , Paul MartinPaul Martin More articles by this author , David HawkesDavid Hawkes More articles by this author , Neil McCartanNeil McCartan More articles by this author , Ingrid PotykaIngrid Potyka More articles by this author , Norman WilliamsNorman Williams More articles by this author , Chris Brew-GravesChris Brew-Graves More articles by this author , Caroline MooreCaroline Moore More articles by this author , Mark EmbertonMark Emberton More articles by this author , and Hashim AhmedHashim Ahmed More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.1016AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Multi-parametric MRI targeted prostate biopsies can improve detection of clinically significant prostate cancer and decrease the diagnosis of clinically insignificant cancers. There is debate whether visual estimated targeting is sufficient or whether image-fusion software is required. We conducted an ethics committee approved, prospective, blinded, paired validating clinical trial of visual estimated targeted biopsies compared to non-rigid MR/US image-fusion using an academically developed fusion system (SmartTarget®). METHODS 141 men requiring targeted transperineal biopsies for accurate risk stratification were enrolled following written informed consent (August2014-September2016). Entry required prior trans-rectal ultrasound biopsy, a discrete lesion on mpMRI (PIRADS 3-5) and no previous prostate cancer treatment. All men underwent a targeted transperineal biopsy of a single lesion using visual estimation (3 cores) and image-fusion (3 cores). Two different urologists independently conducted the targeting strategies with each urologist assigned a strategy and order by randomised. Only one urologist was permitted to be present for each biopsy strategy and did not communicate with the other about any aspect of the procedure (blinded). 14 urologists of varying levels of experience participated. All were competent in transperineal targeting. UCL definition 2 clinically significant prostate cancer was used as the target condition for the primary outcome (>/= G3+4 and or MCCL >/= 4mm). RESULTS 129 men completed both biopsy strategies. Median age (IQR) was 65 years (58-70) and median PSA was 8.5ng/ml (5.6-12.1). 94 (72.8%) had clinically significant prostate cancer; 81/94 (86.2%) were identified on visual estimation targeting and 81/94 (86.2%) on image-fusion targeting. Fusion biopsy and visual estimation targeting each identified clinically significant cancers that the other missed, 13 and 13 respectively. We found no statistically significant difference between visual estimation and image-fusion (McNemar, p=1.00). CONCLUSIONS Our prospective, paired blinded trial demonstrates that there is no overall difference in detection of clinically significant prostate cancer between visual estimation and image-fusion targeted biopsies. Our results suggest that both techniques should be used together for optimal cancer detection. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e425 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Ian Donaldson More articles by this author Sami Hamid More articles by this author Dean Barratt More articles by this author Yipeng Hu More articles by this author Rachel Rodell More articles by this author Barbara Villarini More articles by this author Ester Bonmati More articles by this author Paul Martin More articles by this author David Hawkes More articles by this author Neil McCartan More articles by this author Ingrid Potyka More articles by this author Norman Williams More articles by this author Chris Brew-Graves More articles by this author Caroline Moore More articles by this author Mark Emberton More articles by this author Hashim Ahmed More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Recall and Cancer Detection Rates for Screening Mammography: Finding the Sweet Spot.

The purpose of this study is to identify the optimal screening mammography recall rate range on the basis of cancer detection rates among breast imaging specialists at an academic institution. Medical outcome audit data collected in accordance with the Mammography Quality Standards Act from September 1, 2007, through August 31, 2012, were reviewed. Cancer detection rates were calculated from 984 screen-detected cancers identified in 188,959 total digital screening mammograms. The percentages of minimally invasive and early-stage cancers were also calculated. The 75 annual recall rates were analyzed two ways. First, they were separated into recall groups to assess cancer detection rate variation by the recall categories using rate ratios: less than 10%, 10% to less than 12%, 12% to less than 14%, and 14% or higher. Next, a linear regression with bootstrap bias correction was performed to assess changes in cancer detection rate with each unit increase in the recall rate up to 20%, with the recall category of less than 7% taken as reference. Annual cancer detection rates for a physician were grouped according to annual percentage recall rate. Statistically significantly higher cancer detection rates were seen for recall rates 12% or higher, with rate ratios of 1.75 (95% CI, 1.40-2.19) and 2.06 (95% CI, 1.72-2.46) for the recall groups 12% to less than 14% and 14% and higher, respectively, compared with the less than 10% group. When taking the category 12% to less than 14% as the reference, there were no statistically significant differences between recall groups 12% to less than 14% and 14% or higher in cancer detection rate. A statistically significant increase in the cancer detection rate with each unit increase in the recall rate was seen only for recall rates 12% or higher. These observations suggest that the sweet spot for optimal cancer detection is in the recall rate range 12% to less than 14% with the incremental benefit above this to be relatively small. A recall rate less than 10% may be too low.

Study of the influence of over-the-counter vitamin supplement intake on urine fluorescence to optimize cancer detection by fluorescence cystoscopy.

Fluorescence cystoscopy (FC) efficiently enhances the detection and improves the therapeutic management of early bladder cancer. During an FC, about 150 ml of water is needed to inflate the bladder. The water is quickly diluted by urine which can be fluorescent. If this bladder washout fluid (BWF) becomes fluorescent, the FC images are frequently degraded. Unfortunately, it is unclear which elements of the diet may contribute to this background fluorescence. We propose to start this exploration with over-the-counter (OTC) vitamin supplements. To this end, we measured excitation–emission matrices of urine samples and the kinetics of modifications of urine fluorescence obtained from nine healthy volunteers before, during, and after intake of a commercially available OTC vitamin supplement. The pharmacokinetics shows that the BWF fluorescence values reach a maximum 8 to 10 h after vitamin intake. They decrease in the half-day that follows and reach values close to baseline ~1 day afterward. Based on these results, we conclude that, in order to avoid degradations of fluorescence images, it is likely best that the intake of OTC vitamin supplements be avoided during the week preceding an FC.

Repeated biopsy in the detection of prostate cancer: when and how many cores.

We performed an analysis of the literature about the optimal prostate biopsy (PBX) scheme in the repeated setting We performed a clinical and critical literature review by searching Medline Database from January 2005 up to January 2014. Electronic searches were limited to the English language. The keywords were: prostate cancer, prostate biopsy, transrectal ultrasound, transperineal prostate biopsy. The recommended approach in repeated setting is still the extended scheme (EPBx) (12 cores). An approach with more than 12 cores according to the clinical characteristics of the patients may optimize cancer detection. Saturation PBx (> 20 cores) clearly improves cancer detection if clinical suspicion persists after previous negative biopsy. Nevertheless international guidelines do not strongly recommended SPBx in all situations of repeated setting. EPBx or SPBX may be, in the future, substituted by multiparametric MRI-targeted biopsies. Since the scenario in which a PBx is changing, the issue about the number and location of the cores in PBx is still a matter of debate in repeated setting. At present, EPBx are still the gold standard even if SPBx seems to be necessary in many cases. However, random PBx does not represent the approach of the future, but rather imaging targeted biopsy.

Exhaled breath analysis for lung cancer detection using ion mobility spectrometry.

BackgroundConventional methods for lung cancer detection including computed tomography (CT) and bronchoscopy are expensive and invasive. Thus, there is still a need for an optimal lung cancer detection technique.MethodsThe exhaled breath of 50 patients with lung cancer histologically proven by bronchoscopic biopsy samples (32 adenocarcinomas, 10 squamous cell carcinomas, 8 small cell carcinomas), were analyzed using ion mobility spectrometry (IMS) and compared with 39 healthy volunteers. As a secondary assessment, we compared adenocarcinoma patients with and without epidermal growth factor receptor (EGFR) mutation.ResultsA decision tree algorithm could separate patients with lung cancer including adenocarcinoma, squamous cell carcinoma and small cell carcinoma. One hundred-fifteen separated volatile organic compound (VOC) peaks were analyzed. Peak-2 noted as n-Dodecane using the IMS database was able to separate values with a sensitivity of 70.0% and a specificity of 89.7%. Incorporating a decision tree algorithm starting with n-Dodecane, a sensitivity of 76% and specificity of 100% was achieved. Comparing VOC peaks between adenocarcinoma and healthy subjects, n-Dodecane was able to separate values with a sensitivity of 81.3% and a specificity of 89.7%. Fourteen patients positive for EGFR mutation displayed a significantly higher n-Dodecane than for the 14 patients negative for EGFR (p<0.01), with a sensitivity of 85.7% and a specificity of 78.6%.ConclusionIn this prospective study, VOC peak patterns using a decision tree algorithm were useful in the detection of lung cancer. Moreover, n-Dodecane analysis from adenocarcinoma patients might be useful to discriminate the EGFR mutation.

Breast cancer following ovarian cancer in BRCA mutation carriers.

BRCA mutation carriers are at increased risk of developing breast cancer. However, the incidence of breast cancer after a diagnosis of epithelial ovarian cancer (EOC), one of the tubal/peritoneal cancers collectively referred to as pelvic serous carcinomas, is not well known. Optimal breast cancer surveillance and detection for these patients have also not been well characterized. To determine the incidence of breast cancer after a diagnosis of EOC and to evaluate the need for breast cancer surveillance for these patients. A retrospective database review of 364 patients who underwent BRCA mutation testing for EOC (stages I-IV) between 1998 and 2012 at an academic medical center with gynecologic and breast cancer centers. Incidence of breast cancer and methods of surveillance. Of 364 patients, 135 (37.1%) were found to carry a germline BRCA1 or BRCA2 mutation. The mean age of patients at diagnosis of EOC was 49.5 years (range, 28-89 years). Of the 135 patients, 12 (8.9%) developed breast cancer. The median time from diagnosis of EOC to diagnosis of breast cancer was 50.5 months. Annual mammography was performed for 80 patients (59.3%), with annual magnetic resonance imaging of the breasts performed for 60 patients (44.4%). Thirteen patients (9.6%) underwent a bilateral prophylactic mastectomy at a median of 23 months following EOC diagnosis. Breast cancer was most commonly diagnosed by mammography for 7 of the 12 patients (58.3%), 3 (25.0%) of whom had a palpable mass and 2 (16.7%) of whom had incidental breast cancer detected during a prophylactic mastectomy. Seven patients with breast cancer (58.3%) underwent a bilateral mastectomy. All patients had early-stage breast cancer (stages 0-II). Four patients (33.3%) received adjuvant chemotherapy. At a median follow-up of 6.3 years, 4 of the 12 patients (33.3%) died of recurrent EOC after a diagnosis of breast cancer. The overall 10-year survival rate for the entire cohort of 135 patients was 17.0%. The risk of metachronous breast cancer is low in patients with known BRCA mutations and EOC. A majority of these cases of breast cancer at an early stage are detected by use of mammography. Despite the small number of patients in our study, these results suggest that optimal breast cancer surveillance for patients with BRCA-associated EOC needs to be reevaluated given the low incidence of breast cancer among these high-risk patients. Confirmation of our findings from larger studies seems to be indicated.

Random biopsy: when, how many and where to take the cores?

The optimal random prostate biopsy scheme (PBx) in the initial and repeated setting is still an issue of controversy. We performed an analysis of the recent literature about the prostate biopsy techniques. We performed a clinical and critical literature review by searching MEDLINE database from January 2005 up to January 2014. Electronic searches were limited to the English language, and the keywords prostate cancer, prostate biopsy, transrectal ultrasound, transperineal prostate biopsy were used. Prostate biopsy strategy in initial setting. According to the literature and the major international guidelines, the recommended approach in initial setting is still the extended scheme (EPBx) (12 cores). However, there is now a growing evidence in the literature that (a) saturation PBx (>20 cores) (SPBx) might be indicated in patients with PSA <10 ng/ml or low PSA density or large prostate and (b) an individualized approach with more than 12 cores according to the clinical characteristics of the patients may optimize cancer detection in the single patient. Moreover, in the era of multi-parametric MRI (mpMRI), EPBx or SPBX may be substituted by mpMRI-targeted biopsies that have demonstrated superiority over systematic random biopsies for the detection of clinically significant disease and representation of disease burden, while deploying fewer cores. Prostate biopsy strategy in repeat setting. How and how many cores should be taken in the different scenarios in the repeated setting is still unclear. SPBx clearly improves cancer detection if clinical suspicion persists after previous biopsy with negative findings and is able to provide an accurate prediction of prostate tumour volume and grade. Nevertheless, international guidelines do not strongly recommended SPBx in all situations of repeated setting. In the active surveillance and in focal therapy protocols, the optimal schemes have to be defined. The course of PBx has changed significantly from sextant biopsies to systematic and from extended to SPBx schemes. The issue about the number and location of the cores is still a matter of debate both in initial and in repeat setting. At present, EPBx is sufficient in most of the cases to provide adequate diagnosis and prostate cancer characterization in the initial setting, while SPBx seems to be necessary in repeat setting. The PBx schemes are evolving also because the scenario in which a PBx is necessary is changing. Random prostate PBx do not represent the future, while imaging target biopsy are becoming more popular.

Optimizing prostate cancer detection during biopsy by standardizing the amount of tissue examined per core

To investigate the effect on cancer detection by varying the number of cores taken for prostate biopsy according to the size of the prostate. A retrospective review of a prospectively registered prostate biopsy database identified 3040 consecutive patients undergoing prostate biopsy at a Veterans Administration Hospital between 1994 and 2008. Of 2224 biopsies, 681 (31%) were found to have cancer and 1540 (69%) had negative biopsies. Prostate volume to biopsy core ratios (volume/number of cores) were derived and a comparative analysis was performed to determine the impact on cancer detection rates. The median prostate volume was significantly smaller for those patients diagnosed with prostate cancer than for those with negative biopsies (33 vs 43 cc, P= 0.01). The median number of cores was the same for both groups of patients (median 12, P= 0.66). The median transrectal ultrasonography TRUS size/core ratio was 3.5 [interquartile range (IQR) 2.5] for patients with identified cancer as compared with 4.7 (IQR = 3.9) for those with negative biopsies (P= 0.000). On multivariable logistic regression analysis TRUS size/core ratio had a significant impact on cancer detection with a relative risk ratio of 1.29 (95% confidence interval, 1.1-1.5, P= 0.001) even when controlled for age, race, prostate volume, digital rectal examination and prostate-specific antigen level. Prostate cancer detection can be enhanced by individualizing the number of cores performed to a real-time prostate volume sampling. The present study emphasizes that optimal cancer detection rates were observed when a ratio of 3.5 cc per tissue core was achieved. Proper prospectively designed studies must be performed to further validate these findings.