bluuebunny/crossref_metadata_2025 huggingface dataset:
BRCANet: A deep hybrid network in predicting BRCA1/2 gene mutation of breast cancer with dynamic contrast-enhanced breast MRI.
Authors: Cao Wen-Ming , Wang Xiaojia , Liu Jian , Wang Lili , Zhang Xiaodong , Pan Jiani , Ye Weiwu , Chen Zhanhong , Zheng Yabing , Shao Xiying , Xu Yuanji
Published: June 2022

e13576

Background: BRCA1/BRCA2, representing as an important genetic biomarker of breast cancer (BCA), can provide clinically significant implications for personalized risk assessment, effective treatment option, and prognostic prediction. Methods: We here proposed the BRCANet, a novel end-to-end convolutional neural network for noninvasively determining BRCA1/BRCA2 mutation by integrating clinical, radiomics and deep learning of dynamic contrast-enhanced (DCE) MRI. BRCANet accepts different forms of medical data including clinicopathologic identifications, high throughput radiomics and deep imaging features of breast MRI using a deep hybrid neural network for data/feature integration. Model training and cross-validation was performed in 132 case-controlled BCA patients from two in two tertiary care hospitals, in which clinicopathologic, genomic and image data of BCA lesions were available and center-standardized for study analysis. Results: Results show that a BRCANet-Plus model, embedded with clinicopathologic, radiomics and deep MRI features achieves an arear under curve of (0.783; 95% confidence intervals [CIs], 0.704 - 0.848) for predicting BRCA1/2 mutation, outperforming the compared state-of-the-art methods, i.e., BRCANet derived from image-only data (0.743; 95% CIs, 0.659 - 0.815; p = 0.037), and BRCARad derived from radiomics-only data (0.734; 95% CIs, 0.649-0.807; p = 0.031). After net benefit evaluation, the proposed BRCANet-Plus shows promise to improve diagnostic performance against conventional clinical or image approaches. Conclusions: Therefore, we concluded the presented deep hybrid approach by integrating multimodal clinical-imaging data, especially breast MRI, have a great potential to predict BRCA1/2 mutational status of BCA. This proof-of-concept strategy can be utilized for studying similar clinical questions.
Prediction of pathological grading of high-risk solitary pulmonary nodules based on CT imaging deep learning: A multi-centric study.
Authors: Lin Weihuan , Zhan Weijie , Zhou HaiYu
Published: June 2022

e13577

Background: Histopathological classification of operable early-stage lung adenocarcinoma may influence the intraoperative surgical decision and postoperative management for patients. However, it is difficult to comprehensively evaluate the grade of risk of operable lung adenocarcinoma based on preoperative CT imaging alone. Therefore, we aimed to establish a pathological grading model for patients with pulmonary nodules through preoperative CT imaging. Methods: This was a diagnostic, retrospective, multi-centric study conducted in two independent centers from January 1, 2019, to June 1, 2021. After inclusion and exclusion criteria, a total of 396 patients were included from two centers and were diagnosed as stage IA lung invasive adenocarcinoma (IAC). Patients (n=308) from the Guangdong Provincial People's Hospital with solitary pulmonary nodules were randomly divided into the training cohort (n=222) and the internal validation cohort (n=86). Patients from the Jiangxi Cancer Hospital comprised the external cohort (n=88). Their clinical characteristics and preoperative CT images were collected at the same high-quality standard. Extracted radiomic features from tumor and peritumor areas were selected via the least absolute shrinkage and selection operator (LASSO) algorithm for a radiomics model (RM) construction. Logistic Regression analysis was conducted to develop a novel multi-parameter model (MPM) that integrated clinical characteristics and radiomic features. The area under the curve (AUC) of the receiver operator characteristic curve (ROC) was calculated to compare the prediction performance of models. Results: The MPM composed of clinical characteristics and radiomic features performed better than RM. It achieved good predictive value for high-risk IAC. The AUC of MPM in the training cohort was 0.87 (95%CI, 0.81-0.92), which achieved better identification performance than the RM (See Table). Similarly, model comparisons were conducted in the two validation cohorts in which the MPM gained high AUC values of 0.78 (95%CI, 0.68-0.88) and 0.83 (95%CI, 0.74-0.91), respectively. In addition, the sensitivity of MPM was higher than 80.0% in the three cohorts. Conclusions: The MPM may be a practical and reliable tool for predicting the pathological risk of patients with solitary pulmonary nodules. [Table: see text]
Application of deep learning models on whole slide images uncover new histological markers related to high-risk malignant pleural mesothelioma.
Authors: Hartman Douglas , Le Douget Jean-Eudes , Ye Ye , Li Yaming , Sin-Chan Patrick , Pronier Elodie , Becich Michael
Published: June 2022

e13580

Background: Malignant Pleural mesothelioma (MPM) is a highly aggressive cancer of the pleural surface and represents 80-90% of mesothelioma diagnosis. MPM is broadly subclassified into three histological subgroups (epithelioid, sarcomatoid, biphasic), however tissue heterogeneity has resulted in diagnostic challenges and suboptimal patient care. There are currently no specific histological markers of high/low-risk MPM patients, which is critical in predicting patient prognosis. Methods: Owkin developed MesoNet, a deep learning model that predicts overall survival (OS) of MPM patients from whole slide images (WSI) and trained on the French MESOBANK and TCGA cohorts (Courtiol et al, 2019). In this study, we sought to validate MesoNet’s performance on an independent cohort of 127 WSI stained with haematoxylin/eosin from MPM patients collected at the University of Pittsburgh as part of the National Mesothelioma Virtual Bank (funding by U24OH009077). Patient demographics, survival data, expertly curated pathology annotations were also collected. Results: Our analyses showed that MesoNet predicted OS as risk score based on WSI, which validated high-risk MPM patients exhibited poorer OS, as compared to low-risk patients. Analyses on histological subtypes revealed sarcomatoid and biphasic patients were overrepresented in high-risk groups, as compared to epithelioid patients, which correlates with observed OS data. Notably, histological features associated with high-risk patients revealed tumor pleomorphism and anaplastic nuclear features, whereas low-risk tiles appear to be enriched in tumor infiltrating lymphocytes (TILs) with accompanying stromal proliferation and dense fibrosis. Conclusions: Collectively, our studies validate MesoNet performance on an independent cohort and identify new features related to MPM risk groups, which may inform future treatment stratification and personalization of immunotherapies.
Overcoming barriers in academic-industry partnerships to improve predictive modeling in immuno-oncology.
Authors: Mittendorf Kathleen F. , Micheel Christine , LeNoue-Newton Michele , Rahman Protiva , Fabbri Daniel , Wolber Jan , Osterman Travis John , Fouda Mohamed , Park Ben Ho
Published: June 2022

e13581

Background: In the past decade, immunotherapies have revolutionized oncology practice by prolonging patient survival in previously rapidly fatal cancers. However, severe immune toxicities present a challenge, affecting ̃20% and up to 50% of patients on immune monotherapies and combination immunotherapies, respectively. Oncologists must balance toxicity risk with potential efficacy, and pharmaceutical companies have a vested interest in selecting patients with the highest benefit–risk ratio during trial enrollment. Predictive toxicity–efficacy modeling has the potential to guide trial subject selection and clinical care, yet there remains a need for predictive models that can be practically implemented in these settings. Methods: A common academic–industry contract data–transfer framework—wherein academic medical institutions and industry counterparts act in isolation—creates barriers to development of high-quality algorithms with practical applications. In this framework, 1) academic medical institutions provide patient data as a “data dump;” these data are static and cannot be refined—reducing opportunities for quality control; 2) predictive model outcomes may include artifacts that are not identified; 3) manual curation of patient data is required to accurately replicate the model in real-world settings; and 4) lack of clinician participation reduces the potential clinical applications of models and reciprocal benefit to the academic institution. We outline a more contemporary, engaged approach to unite strengths of both partners to achieve a common goal. Results: In 2019, Vanderbilt University Medical Center and GE Healthcare partnered with the goal of enabling safer and more precise immunotherapy use. As part of this work, we formulated a recipe for academic–industry partnerships that offers unique advantages over a static contract framework. In our iterative, interactive approach, 1) clinical and curation experts meet with industry modelers to dynamically refine deidentified data sets by resolving discrepancies in data from different sources (e.g., manually curated vs. structured data); 2) clinical experts iteratively review outputs of predictive models to identify potential artifacts and refine final models; 3) expert curators iterate with in-house machine-learning experts to create algorithms to automate curation of natural language elements from the identified EHR data; and 4) clinical and industry stakeholders participate in regular meetings with modelers to ensure clinical and trial utility of the modeling approach. Conclusions: Compared to data transfer-only relationships, this partnership framework offers an opportunity to develop more informed, higher quality immunotherapy models with clinical and industry applications.
Using tandem repeat genomic features for cancer signal detection across multiple cancer types.
Authors: Solovyev Victor , Tobias Sidney , Wong Edmund , Yip Shun H , Tang Cheuk Ying
Published: June 2022

e13586

Background: Next generation sequencing methods enable the identification of molecular signatures predictive of cancers. Large-scale cancer genomic projects such as The Cancer Genome Atlas (TCGA) molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types. The resulting data provides an opportunity to uncover a list of recurrent genomic aberrations, such as mutations, amplifications, insertions, and deletions, that can be used in machine learning models for multiple cancer type detection. Previous research has demonstrated significant variability in the stability of tandem repeat sequences between different cancer types, with consequences for gene expression, which is consistent with known oncogenic mechanisms. Methods: Our approach identifies a set of tandem repeats from whole-exome sequencing data to analyze, and computes differences relative to the reference genome in the cancer patients’ samples. For each tandem repeat sequence (referred to hereafter as TRS), we count the frequency of occurrence of the TRS in its reference state, as well as its specific variations (deletions/insertions) in each cancer patient exome sequence. These TRS read counts are the features for our cancer type prediction model. Through filtering out features of lower significance, we condense our training and testing datasets down from a half million possible features per sample, to merely thousands of features common across all the samples. From there, we train multi-class one-vs-all logistic regression models rapidly and optimize our feature selection and preprocessing to maximize predictive accuracy. Results: As can be seen in the Table below, our logistic regression model trained on TRS features enables us to predict cancer type with high accuracy in TCGA patient samples (as compared with random prediction accuracy, which would be approximately 10%). Conclusions: This approach lays the groundwork for novel TRS-based genetic tests for early detection and diagnosis of multiple types of cancer. [Table: see text] [Table: see text]
Oncology clinical trial termination trends: Analysis across the prior 20 years.
Authors: Palmeri Laura , Derkach Sergii , Vidal-Fisher Liat , Xue X.Q , Harb Wael A. , Moss Keren Rachel
Published: June 2022

e13602

Background: Terminations of oncology clinical trials are an unavoidable potential outcome when novel anti-cancer therapeutics are evaluated, yet are a significant burden on cancer patients, institutions, regulators and the industry as a whole. Overall, approximately 1 in 5 trials are terminated – the four main reasons are poor enrollment (PE), business decisions, lack of efficacy, and toxicity. The objective of our study is to describe trends in trial terminations. Methods: We searched Citeline® Trialtrove database for oncology, phase 1-3, industry sponsored trials, started 1Jan2000-31Dec2019 listed as completed or terminated. Data collection was performed in Jan2022. For analysis purposes, they were grouped based on start year into four 5-year periods: I-2000-2004, II-2005-2009, III-2010-2014, IV-2015-2019. Undetermined phase, phase 4 and non-oncology trials were excluded. Parameters collected: phase, reason for termination (more than one could be listed), sponsor (academic involvement; Top20 pharma as determined by Scrip 100 annually). Results: A total of 21,125 trials met search criteria, of which 4,596 (22%) were indicated as terminated. Termination rate increased from period I to II (17% to 24%) and remained at a steady 22% in periods III, IV. When evaluating the rate of termination according to phase, over periods I-II-III-IV we observed an increase in the rate of termination of phase 1/2 trials - 10%, 17%, 19% and 20% respectively. Across other phases, the change in termination rates was similar to change in their proportional representation per period. In terms of reason for termination, PE remained a major reason overall, 1275 trials (28%) were terminated due to poor enrollment with a low rate of 13% in period I, followed by 37%, 29%, 21% in periods II, III, IV. Of the trials that were terminated due to PE, 51% had academic, and 73% had Top20 involvement. This is a higher representation that their respective involvement in trials. Conclusions: According to the results of our analysis, the proportion of oncology industry-sponsored terminated trials remained high over the last 20 years, with early phases (1, 1/2 & 2) being the most affected. PE accounted for almost 1/3 of the overall terminations, with a possible trend of decrease. In terms sponsor type, there appeared to be a higher number of trials terminated due to PE with Top20, and with academic involvement, compared to their representation in the overall numbers. It is yet to be determined whether innovation in oncology early phase drug development, such as biomarker driven trials and seamless phase transitions will impact termination rates. Further analysis is ongoing focusing on early phase oncology trials.[Table: see text]
Summary of virtual tumor board platforms and implementation in neuro-oncology.
Authors: Ekhator Chukwuyem , Kesari Santosh , Tadipatri Ramya , Fonkem Ekokobe , Grewal Jai
Published: June 2022

e13604

Background: Virtual tumor board [VTB] platforms are an important aspect of cancer management. They enable easier access to a multidisciplinary team of experts. VTBs are an emerging resource across various cancer care networks in the United States. Methods: We performed a systematic search of all VTBs incorporating a platform designed for this specific role. We reviewed Xcures, Genomet, Oncolens, Navify Tumor Board, Medical University of South Carolina [MUSC] Virtual Tumor Board, Virtual Tumor Board Tahoe Forest Cancer Center, UC Davis Health Care Center VTB and MassiveBio. Summary data examined include year of launch, demographics, characteristics of cases, average response time, advantages, and how they handle protected health information. Results: 30% of all VTBs examined launched in 2017. All had a HIPAA compliant online environment. Xcures records were reviewed where the median age of female patients was 57yrs and the median age of male patients was 55 years. The data showed that 44% (4.4 out of every 10 patients) with a confirmed treatment chose VTB integrated option. 76% of patients in Xcures registry consist primary CNS tumors. With at least 556 patients in tumor registry which included 46% Glioblastoma cases [96% primary;, 4% secondary], 13% Diffuse midline glioma with H3K27M-mutant cases, 5% Diffuse intrinsic pontine glioma cases, 3% Astrocytoma and Diffuse Astrocytoma cases, 3% Anaplastic astrocytoma cases; Glioma and Diffuse Glioma, 1% Anaplastic Oligodendroglioma cases and < 1% Gliosarcoma, Oligodendroglioma, Pilocytic astrocytoma, Anaplastic pleomorphic Xanthoastrocytoma, Fibrillary astrocytoma, Oligoastrocytoma and Pleomorphic Xanthoastrocytoma cases. In the MUSC VTB, 9 neuro-oncology cases were reviewed. They meet weekly, and average response time was within 24hrs of case review.Patient demographics were not recorded. Genomet Virtual Board de-identifies all patient information; this is a virtual platform primarily focused on neuro-oncology cases. Cases involved a median of 5 specialist most commonly neuro-oncologists, neurosurgeons, radiation oncologists, molecular pathologists and neuroradiologists. Participants had an average of 23 cases reviewed. Case review revealed an age range from 6 months to 84 years [mean age 44.5 years] with 69.6% males and 30.4% females, 43.5% glioblastoma, 8.7% adenocarcinoma, 8.7% infra-tentorial tumor, < 5% each: pineoblastoma, melanoma, hemangioblastoma and pilocytic astrocytoma. Average response time observed in all cases was ≤24 hours. Detailed metrics for number/percentages of brain tumor cases were not available for most other platforms at time of submission. This will be updated at presentation. Conclusions: VTBs have allowed for quicker expert analysis of cases. This has resulted in an accelerated number of cases reviewed with a shortened communication time. More studies are needed to gain additional insight into user engagement
Whole slide histologic grading of breast cancer using convolutional neural networks.
Authors: Demir Gokhan , Iheme Leonard O. , Solmaz Gizem , Yazici Cisem , Tokat Fatma , Çayır Sercan , Bozaba Engin , Tekin Muhammet Eren , Özsoy Gülşah , Ayaltı Samet , Kayhan Cavit Kerem , İnce Ümit , Uzel Burak , Kusetogullari Huseyin
Published: June 2022

e13607

Background: The Nottingham histologic grade (NHG) is a strong prognostic factor in early-stage breast cancer. It consists of nuclear pleomorphism, tubular formation, and mitotic count. We recently developed an artificial intelligence (AI) based automatic grading system. Methods: In this study, we have retrospectively evaluated 179,651 hematoxylin and eosin-stained patches extracted from 402 digitized biopsies of 338 patients with confirmed invasive ductal carcinoma diagnosis. The data was collected at Acıbadem University Hospital between 2017 and 2021. The digitized slides were manually labeled by seven pathologists before being used to train the deep learning models (DL). The pre-trained (on ImageNet) DL architectures which are EfficientNet backbone U-Net, YOLOv5, DenseNet161, and a modified VGG-11 have been fine-tuned with this study’s dataset for the tubule segmentation, nuclei detection, mitosis classification, and the nuclear pleomorphism classification tasks, respectively. Data augmentation for boosting the accuracy was done. Semantic segmentation for tubular formation, object detection for detection of nuclei, and image classification for the mitotic count and nuclear pleomorphism was also performed. Results: The AI-based algorithms have obtained reproducible histologic scores with mean F1 scores, sensitivities, and specificities as presented in Table. Conclusions: The AI-based automatic grading system is accurate in the evaluation of the components of NHG. This system will speed up the pathology workflow in the clinic, provide decision support to pathologists and mitigate the sensitivity associated with the traditional grading process. [Table: see text]
Cost-effectiveness of waiving coinsurance for follow-up colonoscopy after a positive stool-based colorectal screening test in a Medicare population.
Authors: Fendrick A. , Lieberman David A. , Vahdat Vahab , Chen Jing Voon , Ozbay A. Burak , Limburg Paul J.
Published: June 2022

e13624

Background: Commercial insurance plans are required to cover, with no patient cost sharing, a follow-up colonoscopy (COL) after a positive stool-based colorectal cancer (CRC) screening test. Medicare beneficiaries may still be responsible for 20% coinsurance for a follow-up COL, posing a financial barrier to screening completion. Removing the coinsurance may increase screening adherence and improve outcomes. The estimated cost-effectiveness of stool-based CRC screening was compared among adherence scenarios that assumed the status quo (20% coinsurance for follow-up COL) or waived follow-up COL coinsurance. Methods: The CRC-AIM microsimulation model simulated US Medicare beneficiaries undergoing stool-based CRC screening between ages 65-75 years. Outcomes of total costs, total quality adjusted life years (QALYs), life-years gained (LYG), CRC incidence reductions (IR), and CRC mortality reductions (MR) were calculated per 1000 individuals versus no screening using the weighted average of the outcome with multitarget stool DNA, fecal immunochemical test, and fecal occult blood test, scaled by their estimated use. Costs of follow-up COL were assumed to have 20% coinsurance in the status quo scenario. Four comparative scenarios assumed that waiving coinsurance increased published, real-world stool-based screening or follow-up COL adherence rates by 5% or 10%. Results: In scenarios where waiving coinsurance was assumed to increase screening and/or follow-up COL adherence, up to 21 more LYG and greater IR and MR were observed vs the status quo (Table). Total costs in waived coinsurance scenarios (≥$6,398) were comparable to the status quo ($6,449) as the cost savings in CRC care offset the increased COL costs. Total QALYs in waived coinsurance scenarios (≥9.3671) were greater than the status quo (9.3643) because of improved clinical outcomes. Stool-based testing in waived coinsurance scenarios was either dominant (more effective and less costly) or cost-effective at $50,000/QALY with minimal incremental costs vs the status quo. Conclusions: In a simulated Medicare population, clinical CRC outcomes improved and stool-based screening was cost-effective or cost-saving when waiving the 20% coinsurance was assumed to modestly (5%) increase adherence rates for total CRC screening and/or a COL following a positive test. [Table: see text]
The reduced environmental impact of American society of Clinical Oncology’s 2021 virtual annual meeting.
Authors: Lichter Katie , Demeulenaere Sasha , Drew Taylor , Wong Emily , Grover Surbhi
Published: June 2022

e13625

Background: In response to the threat of the COVID-19 pandemic, the 2021 American Society of Clinical Oncology (ASCO) Annual Meeting transitioned to a virtual, online conference. As medical conferences around the world have transitioned to virtual formats, numerous benefits have been uncovered; however, the environmental effect of reduced travel on carbon emissions remains largely unknown. Today, it is estimated that conference attendance accounts for 35% of a scientist’s total carbon footprint. Given that the climate crisis is a growing threat to human health and oncology outcomes, it is imperative to begin to quantify, understand, and promote sustainable practices. We aim to highlight the reduced travel-related greenhouse emissions associated with the transition of ASCO’s 2021 Annual Meeting to a virtual platform in comparison to a hypothetical in-person conference. Methods: Attendee demographic data was collected online from the ASCO 2021 Annual Meeting. The distance traveled per attendee to a hypothetical in-person conference in Chicago, Illinois (the location for the 2022 Annual Meeting) was estimated using reported attendees’ home country and a hypothesized centralized location. Approximate airline miles and associated carbon dioxide (CO2) emissions were calculated using the Environmental Protection Agency’s Greenhouse Gas Tools. It was assumed that all attendees had non-stop airfare and traveled in economy class. The approximate CO2 emissions for the virtual conference was estimated based on assumed internet usage (8 hours/day for the 5-day conference period). Results: A total of 32,950 conference attendees were identified - 14,150 domestic attendees and 16,050 international attendees. For the hypothesized in-person conference, the total carbon footprint for all assumed transportation was determined to be 28,468,031.25 lbs. of CO2 emissions, with an average of 863.0 lbs. of CO2 emissions per attendee. Alternatively, emissions related to virtual conference internet streaming accounted for a total of 436,258 lbs of CO2. Total emissions spared with the transition to a virtual platform was an estimated 28,031,773.25 lbs of CO2., the equivalent emissions of 1,531 U.S. homes’ energy use for one year. Conclusions: Incorporating options for virtual attendance at academic conferences has the potential to significantly reduce carbon emissions. However, many believe that virtual networking cannot replace in-person interactions particularly for early-career attendees. Regardless, our professional societies have an obligation to investigate and promote greater sustainability of our annual meetings.
Tumor marker testing among Medicare beneficiaries with cancer.
Authors: Lu Christine Y. , Jin Robert , Zhang Fang , Argetsinger Stephanie , Burnett-Hartman Andrea N. , Hao Jing , Honda Stacey A. , Neslund-Dudas Christine , Weinmann Sheila
Published: June 2022

e13628

Background: Precision medicine has changed treatment practices for patients with cancer. Clinical guidelines recommend tumor marker testing for many types of cancer given its benefits. Studies have demonstrated that tumor marker testing increases use of appropriate targeted therapies which is associated with improved survival, particularly among patients with advanced or metastatic cancer. In March 2018, Medicare issued a national coverage determination (NCD) for next-generation sequencing to facilitate tumor marker testing. Methods: We conducted a retrospective study to assess tumor marker testing among Medicare beneficiaries with cancer from 03/01/2016 through 02/28/2020. Data were obtained from the Virtual Data Warehouses of 6 United States (US) healthcare systems in the Cancer Research Network, a well-established distributed data network for cancer research. Together these systems provide care to a diverse population of over 5.5 million people in the US. The index date was the first observed cancer diagnosis date recorded in the tumor registry during the study period. Subgroup analyses included those with lung, breast, colorectal, or prostate cancers, or those with advanced, metastatic or recurrent cancer. This is part of a larger project that aims to advance methods for scalable and rigorous evaluation of outcomes of coverage policies for genetic tests. Results: We report results from one health system (03/01/2016-3/31/2018) including Medicare beneficiaries ≥65 years and ≥ 90 enrollment days after the index date. Among 2,277 Medicare beneficiaries with cancer, mean (SD) age was 74 (6.8) years, 1,065 (46.8%) were women, 199 (9.9%) had a tumor marker test within 90 days of the index date, and 1072 (47.1%) started a cancer drug therapy within 90 days of the index date. Among Medicare beneficiaries with lung (n=352), breast (n=361) colorectal (n=135) and prostate (n=326) cancers, proportions of patients having tumor marker tests ranged from 2.2% to 12.8% within 90 days of the index date. Among 572 Medicare beneficiaries with advanced, metastatic or recurrent cancer, 59 (10.3%) had a tumor marker test within 90 days of the index date. Conclusions: The relatively low tumor marker testing rate among Medicare beneficiaries with cancer in this health system is consistent with the literature. Analyses are underway to examine changes in tumor marker testing among Medicare beneficiaries after the implementation of the 2018 Medicare NCD.
The impact of face-to-face evaluation in the clinical assessment of cancer patients.
Authors: Stival Mirella , Nazareth Aguiar Pedro , Fonseca Magalhaes Filho Marcos Aurelio , Del Giglio Auro
Published: June 2022

e13651

Background: Despite the growing popularity of teleoncology in the context of the COVID-19 pandemic, there is great concern about the quality and accuracy of clinical encounters as there is virtually no physical examination (PE) provided. Objectives: To evaluate the primary sites and clinical variables that could predict a higher frequency of abnormal PE findings and a consequent change in conduct, thus allowing the selection of patients who are possibly ineligible for remote care. Methods: Observational and prospective study, conducted at the oncology center of two public hospitals in Brazil. A multiple-choice form, developed by the researchers, was filled in to collect the clinical variables of the patients, data regarding the service, and oncological conduct determined at the end of the consultation. Results: 368 clinical evaluations of patients with cancer were included in the study, with breast, colorectal, and prostate tumors being the most frequent. The physical examination was normal or with alterations already seen in previous consultations in 87% of the cases while 13% had new changes in the PE. Among patients with new changes in the PE, cancer treatment was maintained in 59% of cases. Thus, PE led to a change in antineoplastic therapy in 12 cases (3% of all patients being 1% only because of the PE findings and 2% after completion of a complimentary examination). Statistical analysis showed a significant association between the altered PE and the following variables: primary site breast (n=27; 21%; p<0.01), head and neck (n=7; 26%; p<0, 01) and ovary (n=2; 17%; p<0.01), presence of symptoms (n=31; 21%; p<0.01), and treatment with palliative intent (n=23; 19%; p<0.01). However, only the presence of symptoms showed a positive association with the altered PE and consequent treatment change (p<0.01). Conclusions: Telemedicine seems to be a safe modality in the vast majority of cases, given the large percentage of asymptomatic patients with no changes in the PE during face-to-face care. Regarding symptomatic patients, we suggest priority to in-person care. In those with EC IV tumors undergoing palliative treatment or with breast, head and neck, and ovarian cancer, we recommend evaluating case by case and considering face-to-face care.
Diagnostic signature of circulating miRNAs in glial brain tumors.
Authors: Pushkin Anton A. , Rostorguev Eduard E. , Kavitskiy Sergey E. , Dzhenkova Elena A. , Kit Oleg I. , Engibaryan Marina A. , Kasyanenko Vladimir N.
Published: June 2022

e14027

Background: Circulating microRNAs (miRNAs) are currently being investigated as non-invasive diagnostic biomarkers in a wide range of tumors. However, there is still no sufficiently reliable siRNA signature for routine screening of patients with glial tumors of varying degrees of malignancy. Our study aimed to determine the level of microRNA pattern in the blood plasma of patients with glial tumors, which previously showed aberrant expression in tumors. Methods: Relative expression of twenty miRNAs (hsa-miR-3180, hsa-miR-3180-3p, hsa-miR-6782-5p, hsa-miR-182-5p, hsa-miR-133b, hsa-miR-670-3p, hsa-miR-342-3p, hsa-miR-454-3p, hsa-miR-1246, hsa-miR-433-3p, hsa-miR-195-5p, hsa-miR-29a-3p, hsa-miR -107, hsa-miR-21, hsa-miR-122, hsa-let-7c-5p, hsa-miR-125b-5p, hsa-miR-190b, hsa-miR-196a-3p, hsa-miR-16 -5p) was studied in the blood plasma of patients with glial tumors of various grades of malignancy (diffuse astrocytoma (DA, n=10), anaplastic astrocytoma (AA, n=10) and glioblastoma (GB, n=10)) and patients with menigioma (MEN, n=10) compared with a conditionally healthy group (CNTRL, n=10) by RT-PCR. Cycle thresholds were normalized to exogenous cel-miR-39-5p. Relative expression (RE) was calculated using the 2ΔΔCt method. Wilcoxon's t-test and Benjamini-Hochberg multiple comparison adjustment were used to assess differences. Results: As a result of evaluating the relative expression of the studied miRNAs between groups, differences were found for 9 miRNAs. P-values are listed in the Table. Conclusions: Thus, circulating miRNAs could potentially act as biomarkers for both gliomas and their subtypes and differentiate gliomas from meningiomas. The findings may provide new diagnostic strategies for gliomas. Significance levels of differences in the relative expression of miRNAs when comparing the studied groups with each other.[Table: see text]
Assessment of neurocognitive functions using ACE-III scale in patients with high-grade glioma receiving adjuvant chemoradiation.
Authors: Mandal Shreyosi , Joseph Deepa , Krishnan Ajay S , Singh Pragya , Arora Rajnish , Chauhan Udit , Gupta Sweety , R Lekshmi , Gupta Manoj Kumar
Published: June 2022

e14030

Background: High Grade Gliomas (HGG) are aggressive CNS tumours with poor prognosis despite multimodality management with surgery, radiation and chemotherapy. During the course of the disease and treatment patients experience alterations in neurocognitive functions (NCF) & QOL which is dependent on factors like size, location of tumour, type of surgery, chemo radiotherapy (CTRT) received & disease recurrence. Assessment of change in pattern of NCF is important in optimising treatment and interventions like use of neuroprotectors and in guiding future clinical trials in those directions. Change in NCF significantly affects QOL of patients and their care givers. Most studies on NCF in brain tumours has been conducted on Low Grade Gliomas and various tools have been used in these studies. Addenbrooke Cognitive Examination tool (ACE III) has been used by some in the assessment of NCF in brain tumour. This study aims to assess change in NCF in postoperative HGG patients receiving adjuvant chemoradiation. Methods: 13 newly diagnosed post operative cases of Grade III & IV gliomas were prospectively enrolled in the study. Enrolled patients received adjuvant radiotherapy (RT) (60Gy/30 fractions) using IMRT technique with concurrent Temozolomide (TMZ) 75mg/m2 over 6 weeks. Patients received adjuvant TMZ 150-200 mg/m2 up to six 4-weekly cycles. Pre-treatment Neurocognitive functions were assessed using ACE III scale before start of RT. Post-treatment assessment was done using ACE III at 6 months after completion of RT. The ACE-III scale consists of 5 domains namely Attention (Total Score-18), Memory (TS-26), Fluency (TS-14), Language (TS-26), Visuospatial Abilities (TS-16); the Total Score is given out of 100.The variable are described using mean (SD); Paired t test was used for statistical analysis and baseline data was compared with 6 months’ data. Results: The study group was homogenous with a median age of 42 years. Fronto-parietal region of the brain was most commonly involved. All patients had received concurrent TMZ, completed RT within 6 weeks and received adjuvant TMZ for a period of 6 months. On Comparing baseline to 6 months data; statistically significant improvement was noted in Memory(p-0.049), Language(p-0.031) & the Total Score (p-0.048).There was improvement in total ACE III score in 84% of the patients at 6 months. Conclusions: Significant improvement noted in majority of the postoperative patients at 6 months’ post RT in both total ACE III score and specific domains except a few. Long term follow up needed to assess further change in NCF due to disease or long term treatment related effects.[Table: see text]
Evaluation of phase 2 single-arm two-stage trials in glioblastoma and brain tumor.
Authors: Yoo Wonsuk , Kim Seongho , Mehta Shwetal , Sanai Nader
Published: June 2022

e14033

Background: Oncology clinical trials have shown the lowest success rate on Phase 2 and the highest median expense in a new drug to market. We aimed to assess the appropriate implementation of single-arm two-stage Phase 2 trials with other critical design issues of phase 2 single-arm trials such as selection of primary endpoint and historical control rates, and challenges of the blood-brain barrier, and to suggest guidelines to handle design issues related to single-arm Phase 2 trials in Neuro-Oncology. Methods: Two researchers searched with the key words of “phase 2 single arm two-stage design in glioblastoma” through PubMed.gov, clinicaltrials.gov, and Google Scholar to examine study designs of (i) study information, (ii) two-stage design parameters, (iii) rejection numbers and sample size for each stage. Design issues in primary endpoints, historical controls, and challenges on brain tumor trials were discussed with the effect of adaptive design on two-stage designs, and several guidelines on these issues were suggested for investigators in Phase 2 single-arm Neuro-Oncology trials. Results: Almost 50% of single-arm two-stage studies failed to provide key information related to study designs and sample size calculation, even wrong power analysis and interpretation. Response rate and progression-free survival are used as popular surrogate endpoints for overall survival. It is time to evaluate other potential endpoints like post-progress survival and hazard ratio, and usefulness of co-primary endpoint. Investigators should keep transparency of the trials by fully providing and citing historical data source. If there exist large variabilities among existing historical data, meta-analysis can be a good option to accurately estimate the historical control. Conclusions: The appropriate selection on primary endpoint(s) and historical control data, incorporation of adaptive designs if necessary, and right application of two-stage design might help overcoming the challenges on low success rates in new therapeutic oncology trials. Furthermore, the new paradigm of Phase 0 trial might be one potential tool to overcome the challenges with insufficient penetration of therapeutic agents across the blood-brain barrier and limitations in the applicability of animal models to the human condition in glioblastoma and Neuro-Oncology.
Variant allelic fraction of driver mutations predicts success of genomic methylation classification in CNS tumors.
Authors: Jamshidi Pouya , McCord Matthew , Lukas Rimas Vincas , Santana-Santos Lucas , Jennings Lawrence , Horbinski Craig M.
Published: June 2022

e14035

Background: Whole genome CpG DNA methylation profiling is proving to be an extremely valuable tool in the diagnostic and prognostic classification of central nervous system (CNS) tumors, and is especially adept at discriminating between very different tumors, or tumor subtypes, that are histologically similar. However, like any bulk assay, reliability of methylation-based classification depends on sufficient tumor cellularity, as admixed nonneoplastic elements (e.g., neurons, glial cells, inflammatory cells) have their own unique methylation profiles, and thus can skew the results. Currently, it is recommended that at least 70% of the analyzed specimen be tumor; however, this is based on subjective estimates from light microscopy. Many neoplastic entities have well-known driver mutations that occur in virtually 100% of tumor cells, and next-generation sequencing (NGS) assays can detect those mutations and report their relative amounts in the form of Variant Allelic Fraction (VAF). Since NGS and methylation profiling are now frequently being done on the same tumor block, we sought to determine whether driver mutation VAF affects the accuracy of methylation profiling, and whether VAF can help establish more rigorous cutoffs for quality assurance. Methods: Using NGS and Infinium Epic850K methylation arrays, we evaluated 62 CNS neoplasms representing a range of entities, including TERT promoter-mutant glioblastoma, IDH-mutant astrocytoma, IDH-mutant oligodendroglioma, SHH-driven medulloblastoma, and CTNNB1-driven adamantinomatous craniopharyngioma. Results: VAFs of each driver mutation ranged between 1-60%. Forty-four of 62 cases (71%) had a methylation classification score ≥0.9, the most widely accepted cutoff for a successful result. A fit-of-mixture analysis via CutoffFinder (PMID: 23251644) suggested that the optimal VAF cutoff is 21%. Forty-one of 46 (89%) cases with a VAF of 21% or higher had a methylation classification score ≥0.9, whereas only 3/16 (19%) at or below 21% were classifiable with methylation profiling ( P< 0.0001 by Fisher’s exact test). Nonlinear regression of VAF versus methylation score by least squares fit produced R2= 0.54, with sum of squares = 2.0. Conclusions: These data indicate that VAF can serve as a useful predictor of classification success via methylation profiling, and should be taken into account when interpreting methylation profiling results.
Dose-dependent efficacy of bevacizumab in recurrent glioblastoma.
Authors: Melhem Jawad , Tahir Ali , Calabrese Eirena , Granovskaya Inga , Atenafu Eshetu G. , Sahgal Arjun , Lim-Fat Mary Jane , Perry James R.
Published: June 2022

e14042

Background: Bevacizumab (bev), at a standard dose of 10 mg/kg every 2 weeks is associated with prolonged progression free survival (PFS) but no improvement in overall survival (OS) in recurrent glioblastoma (rGBM). While there are reports of lower dose regimens of bev being better tolerated and effective in rGBM, this has not been studied in comparison to a standard dose of bev. The main aim of this study is to report on the dose-dependent efficacy of bev by comparing PFS and OS in rGBM patients receiving standard dose (SD) vs lower dose (LD) of bev. Methods: A single-center retrospective study of patients with rGBM started on bev between 2015 – 2020 was performed. Clinical and treatment data including bev dose regimen (SD [10 mg/kg every 2 weeks] vs LD [5 mg/kg every 2-3 weeks or 10 mg/kg every 3 weeks]) received at the time of rGBM diagnosis were captured. Overall survival (OS) and progression-free survival (PFS) on bev were compared between the two groups using the Kaplan-Meier product-limit method. Log-rank test was used to compare potential predictive factors. Cox regression model was performed for multivariable analysis of OS and PFS. Results: A total of 96 patients were included with a median follow-up duration of 6.84 months (range 1.12-50.63 months) from date of the first infusion. The LD group consisted of 55/96 patients (57 %) and the age in the LD group was significantly older than the SD group (62 vs 54 years p = 0.009). There was no significant difference in MGMT status between the 2 groups (p = 0.73). Eight patients received lomustine with bev (3 from the standard and 5 from the low dose group). The LD group had prolonged OS (10.23 months versus 6.28 months; p-value = 0.0010) and PFS (5.89 months versus 3.22 months; p-value = 0.0112). Multivariable analysis including dose of bev, extent of resection, gender and age revealed that standard dose of bev, subtotal resection and female sex were associated with worse OS. Eleven patients in the SD group vs 15 patients in the LD group reported an adverse event related to bev. Conclusions: In this study we demonstrate that a reduced dose of bev (5 mg every 2-3 weeks) prolonged both the OS and PFS compared to a standard dose of 10 mg/kg every 2 weeks. This may represent a better and more cost-effective option for patients with rGBM in need of salvage therapy.
Urinary potassium and association with methotrexate (MTX) elimination delay in patients with primary central nervous system lymphoma (PCNSL).
Authors: Harlay Vincent , Boucard Celine , Petrirena Gregorio , Barrie Maryline , Campello Chantal , Autran Didier , Gouton Etienne , Chinot Olivier L. , Tabouret Emeline
Published: June 2022

e14045

Background: First-line treatment of PCNSL consists in a polychemotherapy based on high-dose MTX with hyperhydratation until MTX elimination. Renal toxicity remains frequent with pejorative impact on renal function and patient survival. Our objective was to determine urinary predictive factors of MTX elimination delay. Methods: Patients with PCNSL followed in our institution were prospectively recruited from December 2020 to May 2021. Daily serum and urinary creatinine and ionogram were collected from the day before MTX administration until elimination. Patients characteristics, biological data and MTX concentration were recorded. Normal elimination time was defined by MTX ≤ 0.05 at 72 hours. Results: We collected data from 64 MTX cycles corresponding to 20 patients. Median age was 71 years (range, 33 - 86) and median Karnofsky Performance Status (KPS) was 70. Forty-four percent of cycles were full dose (3500 mg/m2). Median urinary osmolarities one day before, the day and 24 hours after MTX administration were 325 mOsm (range, 62 - 542), 316 mOsm (range, 58 - 671) and 291 mOsm (range, 56 - 554), respectively. Median time for MTX elimination was 94.7 hours (range, 47 - 205). Only one patient needed to receive MTX antidote (glucarpidase). The factors significantly associated with MTX elimination time were age (p < 0.001), KPS (p < 0.001) and urinary K+ concentration at 24 hours post MTX administration (u-K+) (p = 0.008). Using ROC curve analyses, we determine optimal cutoffs for age (80 years, p < 0.001, AUC = 0.825), KPS (70, p < 0.001, AUC = 0.808) and u-K+ (17.2 mmol/L, p = 0.008 AUC = 0.712). This u-K+ cutoff presented with a specificity of 81% and a sensibility of 60%. Focusing on patients aged < 80 years (52 cycles / 64), we confirmed that both KPS (p = 0.022) and u-K+ (p = 0.027) were significantly correlated with MTX elimination time by multivariate analysis. Then, we derived a 3-factors score including 5 subgroups: 1) aged ≥ 80 (N = 12); 2) age < 80, KPS < 70 and high u-K+ (N = 11); 3) age < 80, KPS < 70 and low u-K+ (N = 6); 4) age < 80, KPS ≥ 70 and high u-K+ (N = 8); 5) age < 80, KPS≥70 and low u-K+ (N = 21). The median times for MTX elimination were 112, 96, 95, 82 and 72 hours respectively. The normal elimination rates in each subgroup were 17%, 0%, 17%, 37% and 71% respectively. Conclusions: Age, KPS and u-K+ were correlated with MTX elimination time. We derived a predictive score based on these items that could be used in routine to anticipate MTX elimination delay and to potentially prevent the risk of renal toxicity. These results need to be prospectively confirmed in a larger cohort.