Molecular alterations in Hürthle cell nodules and preoperative cancer risk

Hürthle cell carcinoma (HCC) is a distinct type of thyroid cancer genetically characterized by DNA copy number alterations (CNA), typically of genome haploidization type (GH-type). However, whether CNA also occurs in benign Hürthle cell adenomas (HCA) or Hürthle cell hyperplastic nodules (HCHN), and have diagnostic impact in fine-needle aspiration (FNA) samples, remains unknown. To address these questions, we (1) analyzed 26 HCC, 24 HCA, and 8 HCHN tissues for CNA and other mutations using ThyroSeq v3 (TSv3) next-generation sequencing panel, and (2) determined cancer rate in 111 FNA samples with CNA and known surgical outcome. We identified CNA, more often of the GH-type, in 81% of HCC and in 38% HCA, but not in HCHN. Among four HCC with distant metastasis, all had CNA and three TERT mutations. Overall, positive TSv3 results were obtained in 24 (92%) HCC, including all with ATA high risk of recurrence or metastasis. Among 111 FNA cases with CNA, 38 (34%) were malignant and 73 (66%) benign. A significant correlation between cancer rate and nodule size was observed, particularly among cases with GH-type CNA, where every additional centimeter of nodule size increased the malignancy odds by 1.9 (95% CI 1.3–2.7; P = 0.001). In summary, the results of this study demonstrate that CNA characteristic of HCC also occur in HCA, although with lower frequency, and probability of cancer in nodules with CNA increases with nodule size. Detection of CNA, in conjunction with other mutations and nodule size, is helpful in predicting malignancy in thyroid nodules.

Retrospective analysis of the effect on interval cancer rate of adding an artificial intelligence algorithm to the reading process for two-dimensional full-field digital mammography

Interval cancers are a commonly seen problem in organized breast cancer screening programs and their rate is measured for quality assurance. Artificial intelligence algorithms have been proposed to improve mammography sensitivity, in which case it is likely that the interval cancer rate would decrease and the quality of the screening system could be improved. Interval cancers from negative screening in 2011 and 2012 of one regional unit of the national German breast cancer screening program were classified by a group of radiologists, categorizing the screening digital mammography with diagnostic images as true interval, minimal signs, false negative and occult cancer. Screening mammograms were processed using a detection algorithm based on deep learning. Of the 29 cancer cases available, artificial intelligence identified eight out of nine of those classified as minimal signs, all six false negatives and none of the true interval and occult cancers. Sensitivity for lesions judged to be already present in screening mammogram was 93% (95% confidence interval 68–100) and sensitivity for any interval cancer was 48% (95% confidence interval 29–67). Using an artificial intelligence algorithm as an additional reading tool has the potential to reduce interval cancers. How and if this theoretical advantage can be reached without a negative effect on recall rate is a challenge for future research.

Changes in cancer incidence and mortality in Australia over the period 1996–2015

Objective A previous Australian study compared the observed numbers of cancer cases and deaths in 2007 with the expected numbers based on 1987 rates. This study examines the impact of cancer rate changes over the 20-year period 1996–2015, for people aged under 75 years. Results The overall age-standardised cancer incidence rate increased from 350.7 in 1995 to 364.4 per 100,000 in 2015. Over the period 1996–2015, there were 29,226 (2.0%) more cases (males: 5940, 0.7%; females: 23,286, 3.7%) than expected numbers based on 1995 rates. Smaller numbers of cases were observed compared to those expected for cancers of the lung for males and colorectum, and cancers with unknown primary. Larger numbers of cases were observed compared to those expected for cancers of the prostate, thyroid and female breast. The overall age-standardised cancer mortality rate decreased from 125.6 in 1995 to 84.3 per 100,000 in 2015. During 1996 to 2015 there were 106,903 (− 20.6%) fewer cancer deaths (males: − 69,007, − 22.6%; females: − 37,896, − 17.9%) than expected based on the 1995 mortality rates. Smaller numbers of deaths were observed compared to those expected for cancers of the lung, colorectum and female breast, and more cancer deaths were observed for liver cancer.

Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research

Ground water contamination with Arsenic (As) is one of the foremost issues in the South Asian countries where ground water is one of the foremost sources of drinking water. In Asian countries, especially people of Pakistan living in rural areas are devouring ground water for drinking purpose, and cleaned water is not accessible to them. This arsenic contaminated water is hazardous for human health. The persistence of this study is to study the increasing level of arsenic in ground water in coming years for Khairpur, Sindh Pakistan, which is also increasing the cancer rate (skin cancer, blood cancer) gradually in human body. To predict the arsenic value and cancer risk for the next five years, we have developed two models via Microsoft Azure machine learning with algorithms include Support Vector Machine (SVM), Linear Regression (LR), Bayesian Linear Regression (BLR), Boosted Decision tree (BDT), exponential smoothing ETS, Autoregressive Integrated Moving Average (ARIMA). The developed predictive model named as Arsenic Contamination and Cancer Risk Assessment Prediction Model (ACCRAP model) will help us to forecast the arsenic contamination levels and the cancer rate. The results demonstrated that BLR pose highest prediction accuracy of cancer rate among the four deployed machine learning algorithms.

Interval Cancer Rate and Diagnostic Performance of Fecal Immunochemical Test According to Family History of Colorectal Cancer

Background: The potential role of the fecal immunochemical test (FIT) in individuals with a family history of colorectal cancer (CRC) remains unclear. We assessed interval cancer rate (ICR) after the FIT and FIT diagnostic performance according to family history of CRC. Methods: Using the Korean National Cancer Screening Program Database, we collected data on subjects who underwent the FIT between 2009 and 2011. The interval cancer rate (ICR) was defined as the number of subjects diagnosed with CRC within 1 year after the FIT per 1000 subjects with negative FIT results. Results: Of 5,643,438 subjects, 224,178 (3.97%) had a family history of CRC. FIT positivity rate (6.4% vs. 5.9%; adjusted relative risk (aRR) 1.11; 95% confidence interval (CI) 1.09–1.13) and ICR (1.4 vs. 1.1; aRR 1.43 (95% CI 1.27–1.60)) were higher in these subjects than in those with no such history. These results were the same regardless of whether subjects had undergone colonoscopy within the last 5 years before the FIT. However, the diagnostic performance of the FIT for CRC, as measured using the area under the operating characteristic curve, was similar between subjects without a family history and those with one (85.5% and 84.6%, respectively; p = 0.259). Conclusion: the FIT was 1.4 times more likely to miss CRC in subjects with a family history than in those without (aRR 1.43 for ICR), although its diagnostic performance was similar between the two groups. Our results suggest that for individuals with a family history of CRC, colonoscopy should be preferred over FIT for both screening and surveillance.