cancer surveillance
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BackgroundPatients with long-duration ulcerative colitis (UC) had a higher risk of developing ulcerative colitis-associated carcinogenesis (UCAC) when compared to those with short-duration UC. This study aimed to discover the biomarker for cancer surveillance related to disease duration.MethodsThe microarrays were divided into short-duration (<10 years) UC, long-duration (≥10 years) UC, UCAC, and normal groups in the Gene Expression Omnibus (GEO) datasets. Differentially expressed genes (DEGs) of GEO and the hub genes of the selected weighted gene co-expression network analysis (WGCNA) were intersected to obtain the overlapping genes. Among these genes, the key gene was identified by using the protein–protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the cytoHubba of Cytoscape, and the expression levels. Also, immunofluorescence of human colonic mucosa and animal experiment were used to validate the expression trend of the key gene in the progress of UC developing into UCAC.ResultsLipocalin-2 (LCN2) was more relevant with disease duration of UC and significantly negatively correlated with the risk of UCAC. The expression level of LCN2 in short-duration UC was higher than that of long-duration UC (P < 0.01), long-duration UC was higher than that of UCAC (P = 0.001), and UC and UCAC were all higher than that of the normal (P < 0.001). We then discovered that the expression trend of LCN2 in blood and stool samples was consistent with that in colorectal mucosa.ConclusionThe research indicates that LCN2 could be a novel biomarker to evaluate cancer surveillance related to disease duration of developing UC into UCAC. Compared with that of blood samples, stool detection of LCN2 may have more advantages for diagnosis value of early stage of UCAC as a complement to colonoscopy surveillance.

The association between multi-dimensional deprivation and public health is well established, and many area-based indices have been developed to measure or account for socioeconomic status in health surveillance. The Yost Index, developed in 2001, has been adopted in the US for cancer surveillance and is based on the combination of two heavily weighted (household income, poverty) and five lightly weighted (rent, home value, employment, education and working class) indicator variables. Our objectives were to 1) update indicators and find a more parsimonious version of the Yost Index by examining potential models that included indicators with more balanced weights/influence and reduced redundancy and 2) test the statistical consistency of the factor upon which the Yost Index is based. Despite the usefulness of the Yost Index, a one-factor structure including all seven Yost indicator variables is not statistically reliable and should be replaced with a three-factor model to include the true variability of all seven indicator variables. To find a one-dimensional alternative, we conducted maximum likelihood exploratory factor analysis on a subset of all possible combinations of fourteen indicator variables to find well-fitted one-dimensional factor models and completed confirmatory factor analysis on the resulting models. One indicator combination (poverty, education, employment, public assistance) emerged as the most stable unidimensional model. This model is more robust to extremes in local cost of living conditions, is comprised of ACS variables that rarely require imputation by the end-user and is a more parsimonious solution than the Yost index with a true one-factor structure.

BackgroundBRCA1/2 mutation carriers are suggested with regular breast cancer surveillance screening strategies using mammography with supplementary MRI as an adjunct tool in Western countries. From a cost-effectiveness perspective, however, the benefits of screening modalities remain controversial among different mutated genes and screening schedules.MethodsWe searched the MEDLINE/PubMed, Embase, Cochrane Library, Scopus, and Web of Science databases to collect and compare the results of different cost-effectiveness analyses. A simulated model was used to predict the impact of screening strategies in the target group on cost, life-year gained, quality-adjusted life years, and incremental cost-effectiveness ratio (ICER).ResultsNine cost-effectiveness studies were included. Combined mammography and MRI strategy is cost-effective in BRCA1 mutation carriers for the middle-aged group (age 35 to 54). BRCA2 mutation carriers are less likely to benefit from adjunct MRI screening, which implies that mammography alone would be sufficient from a cost-effectiveness perspective, regardless of dense breast cancer.ConclusionsPrecision screening strategies among BRCA1/2 mutation carriers should be conducted according to the acceptable ICER, i.e., a combination of mammography and MRI for BRCA1 mutation carriers and mammography alone for BRCA2 mutation carriers.Systematic Review RegistrationPROSPERO, identifier CRD42020205471.

Oncuria™ is a validated quantitative multiplex immunoassay capable of detecting bladder cancer from a voided urine sample. Herein, we sought to determine whether Oncuria™ affects physicians’ use of non-invasive and invasive diagnostic tests for microhematuria, gross hematuria, and bladder cancer surveillance. We conducted a survey-based study to assess physician management of nine clinical scenarios involving real-world data from patients with gross hematuria, microhematuria, and bladder cancer on surveillance. We randomly sampled 15 practicing urologists and generated data including 135 patient-by-urologist interactions and 2160 decision points. Urologists recommended a selection of diagnostic tests and procedures before and after Oncuria™ results were provided. We assessed changes in provider use of non-invasive and invasive diagnostic tests after Oncuria™ results were provided. Over 90% of all urologists changed their diagnostic behavior in at least one patient case with the addition of Oncuria™ results. The total number of diagnostic procedures was reduced by 31% following the disclosure of a negative Oncuria™ test and 27% following the disclosure of a positive Oncuria™ test. This is pilot study has the potential to shed light on the analysis of our four large multicenter international studies deploying OncuriaTM. The Oncuria™ urine-based test, a molecular diagnostic capable of ruling out the presence of bladder cancer, reduces both unnecessary invasive and non-invasive diagnostics and has the potential to reduce costs and improve patient outcomes.

PURPOSE: To examine the ability of 5 artificial intelligence (AI)-based computer vision algorithms, most trained to detect visible breast cancer on mammograms, to predict future risk relative to the Breast Cancer Surveillance Consortium clinical risk prediction model (BCSC v2). PATIENTS AND METHODS: In this case-cohort study, women who had a screening mammogram in 2016 at Kaiser Permanente Northern California with no evidence of cancer on final imaging assessment were followed through September 2021. Women with prior breast cancer or a known highly penetrant gene mutation were excluded. From the 329,814 total eligible women, a random subcohort of 13,881 women (4.2%) were selected, of whom 197 had incident cancer. All 4,475 additional incident cancers were also included. Continuous AI-predicted scores were generated from the index 2016 mammogram. Risk estimates were generated with the Kaplan-Meier method and time-varying area under the curve [AUC(t)]. RESULTS: For incident cancers at 0-1 year (interval cancer risk), BCSC demonstrated an AUC(t) of 0.62 (95% CI, 0.58-0.66), and the AI algorithms had AUC(t)s ranging from 0.66-0.71, all significantly higher than BCSC (P < .05). For incident cancers at 1 to 5 years (5-year future cancer risk), BCSC demonstrated an AUC(t) of 0.61 (95% CI, 0.60-0.62), and the AI algorithms had AUC(t)s ranging from 0.63 to 0.67, all significantly higher than BCSC. Combined BCSC and AI models demonstrated AUC(t)s for interval cancer risk of 0.67-0.73 and for 5-year future cancer risk of 0.66-0.68. CONCLUSION: The AI mammography algorithms we evaluated had significantly higher discrimination than the BCSC clinical risk model for interval and 5-year future cancer risk. Combined AI and BCSC models had slightly higher discrimination than AI alone.

Background: Today, ruptured hepatocellular carcinoma (HCC) is a less frequently encountered problem globally due to availability of cancer surveillance protocols for the high-risk population. However, in Thailand, a number of patients do not enroll in screening programs, leading to high rates of ruptured complications. In fit-for-surgery and clinically stable patients, hepatectomy means long-term survival. This study aimed to identify predictive factors of survival in resected patients. Methods: A retrospective review of patients with ruptured HCC who underwent liver resection between January 2013 and December 2019 at Siriraj Hospital was performed. The clinical data and outcomes of patients were analyzed. Results: A total of forty-five patients with ruptured HCC underwent resection or 9.8% (45/460) of all operable HCC cases. There were 6 patients (14.3%) who suffered from postoperative liver failure and one patient (2.4%) died within 30 days. Overall survival (OS) and recurrence-free survival were 90%, 64%, 52% and 42.5%, 24%, 16% at 1, 3, and 5 years, respectively. The factors affecting OS were tumor size > 10 cm, vascular invasion, and positive resection margin. Conclusion: Ruptured HCC is treatable disease and surgical resection plays a major role in good outcomes in patients.

ObjectiveGiven the inconvenience and financial burden of frequent ovarian cancer surveillance and the risks of in-person visits due to COVID-19, which have led to the acceleration of telehealth adaptation, we sought to assess the role of in-person physical examination for the detection of ovarian cancer recurrence among patients enrolled in a routine surveillance program.MethodsThis was a retrospective study of patients initially seen from January 2015 to December 2017 who experienced ovarian cancer recurrence during first clinical remission. Descriptive statistics and bivariate analyses were performed to compare differences in detection methods and in patient and disease characteristics.ResultsAmong 147 patients who met our inclusion criteria, there were no recurrences detected by physical examination alone. Forty-six (31%) patients had recurrence first detected by tumor marker, 81 (55%) by radiographic scan, 17 (12%) by presentation of new symptoms, and 3 (2%) by biopsies taken during non-oncological surgery. One hundred and eleven patients (75%) had multiple positive findings at the time of recurrence. Of all 147 patients, 48 (33%) had symptoms, 21 (14%) had physical examination findings, 106 (72%) had increases in tumor markers, and 141 (96%) had changes on imaging.ConclusionsIn-person physical examination was not a primary means of detection for ovarian cancer recurrence for any patient. Substituting in-person visits for virtual visits that include patient-reported symptoms, alongside a regular surveillance protocol that includes tumor marker testing and imaging, may be a suitable approach for the detection of ovarian cancer recurrence while also reducing patient inconvenience and risks to health.