• Breast neoplasm
• Case study
• Genetic screening
• Li Fraumeni syndrome
• Neoadjuvant therapy
• TP53 protein

• Breast MRI
• Breast cancer
• Breast cancer surveillance
• False negative
• Mammography
• Screening

• Risk Factors
• Breast Neoplasms/classification
• Breast Neoplasms/diagnosis
• Breast Neoplasms/pathology
• False Negative Reactions
• Diagnostic Errors/statistics & numerical data
• Time Factors
• Cohort Studies
• Mammography/standards
• Humans
• Female
• Middle Aged
• Adult
• Aged
• Aged, 80 and over
• Magnetic Resonance Imaging
• Ultrasonography, Mammary
• Imaging, Three-Dimensional

• Breast Neoplasms
• Prognosis
• Survival

• SMC1230451 Samsung Medical Center
• Ministry of Science and ICT, South Korea

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew’s correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen’s kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively.

• Adenocarcinoma/diagnosis
• Adenocarcinoma/genetics
• Breast Neoplasms/diagnosis
• Breast Neoplasms/genetics
• Carcinogens
• Deep Learning
• Female
• Humans
• Male
• Mutation
• Nucleotides

Breast adenocarcinoma is the most common of all cancers that occur in women. According to the United States of America survey, more than 282,000 breast cancer patients are registered each year; most of them are women. Detection of cancer at its early stage saves many lives. Each cell contains the genetic code in the form of gene sequences. Changes in the gene sequences may lead to cancer. Replication and/or recombination in the gene base sometimes lead to a permanent change in the nucleotide sequence of the genome, called a mutation. Cancer driver mutations can lead to cancer. The proposed study develops a framework for the early detection of breast adenocarcinoma using machine learning techniques. Every gene has a specific sequence of nucleotides. A total of 99 genes are identified in various studies whose mutations can lead to breast adenocarcinoma. This study uses the dataset taken from 4127 human samples, including men and women from more than 12 cohorts. A total of 6170 mutations in gene sequences are used in this study. Decision Tree, Random Forest, and Gaussian Naïve Bayes are applied to these gene sequences using three evaluation methods: independent set testing, self-consistency testing, and tenfold cross-validation testing. Evaluation metrics such as accuracy, specificity, sensitivity, and Mathew’s correlation coefficient are calculated. The decision tree algorithm obtains the best accuracy of 99% for each evaluation method.

• Cancer
• breast
• cost
• quality
• value

Breast cancer is the most common cause of mortality due to cancer for women both in Lithuania and worldwide. Chances of survival after diagnosis differ significantly depending on the stage of disease at the time of diagnosis. Extended term periods are required to estimate survival of, e.g., 15–20 years. Moreover, since mortality of the average population changes with time, estimates of survival of cancer patients derived after a long period of observation can become outdated and can be no longer used to estimate survival of patients who were diagnosed later. Therefore, it can be useful to construct analytic functions that describe survival probabilities. Shorter periods of observation can be enough for such construction. We used the data collected by the Lithuanian Cancer Registry for our analysis. We estimated the chances of survival for up to 5 years after patients were diagnosed with breast cancer in Lithuania. Then we found analytic survival functions which best fit the observed data. At the end of this paper, we provided some examples for applications and directions for further research. We used mainly the Kaplan–Meier method for our study.

• Kaplan–Meier estimate
• breast cancer
• cancer awareness campaign
• central death rate
• exposed to risk
• survival analysis

• breast cancer
• breast cancer treatment
• clinical practice guidelines
• consensus
• shared decision making

• Bibliometrics
• Breast Neoplasms/therapy
• Consensus
• Decision Making
• Decision Making, Shared
• Female
• Humans
• Language

Previously, a consolidated mathematical model of primary tumor (PT) growth and secondary distant metastasis (sdMTS) growth in breast cancer (BC) (CoMPaS) was presented. The aim was to detect the diagnostic periods for visible sdMTS via CoMPaS in patients with different subtypes ER/PR/HER2/Ki-67 (Estrogen Receptor/Progesterone Receptor/Human Epidermal growth factor Receptor 2/Ki-67 marker) of breast cancer. CoMPaS is based on an exponential growth model and complementing formulas, and the model corresponds to the tumor-node-metastasis (TNM) staging system and BC subtypes (ER/PR/HER2/Ki-67). The CoMPaS model reflects (1) the subtypes of BC, such as ER/PR/HER2/Ki-67, and (2) the growth processes of the PT and sdMTSs in BC patients without or with lymph node metastases (MTSs) in accordance with the eighth edition American Joint Committee on Cancer prognostic staging system for breast cancer. CoMPaS correctly describes the growth of the PT in the ER/PR/HER2/Ki-67 subtypes of BC patients and helps to calculate the different diagnostic periods, depending on the tumor volume doubling time of sdMTS, when sdMTSs might appear. CoMPaS and the corresponding software tool can help (1) to start the early treatment of small sdMTSs in BC patients with different tumor subtypes (ER/PR/HER2/Ki-67), and (2) to consider the patient almost healthy if sdMTSs do not appear during the different diagnostic periods.

• ER/PR/HER2/Ki-67
• breast cancer
• mathematical model
• secondary distant metastases
• survival
• tumor volume doubling time

• Breast
• Cervical
• Domestic abuse
• Intimate partner violence

• CT scan
• Follow-up
• Hepatocellular carcinoma
• MW ablation
• Surveillance

The National Lung Screening Trial (NLST) was a large, randomized, controlled study showing a 20% reduction of lung cancer mortality and 7% reduction of all cause mortality using annual low dose computed tomography (LDCT) in a high risk population. NLST excluded people with a previous history of cancer treatment within the past 5 years and all people with a history lung cancer. The aim of this work is to review how lung cancer screening trials addressed the confounding effect of previous malignancy. We also review the subsequent recommendations by the United States Preventative Task Force Services, multiple professional societies and the Center for Medicaid and Medicare Services which defer either to NLST criteria or, clinician judgment or refrain from asserting any recommendation on the topic, respectively. Implications of lung cancer screening in the setting of previous malignancies, specifically lung, head and neck, esophageal, gastric, breast, colorectal cancer and lymphoma are also discussed. With lung cancer screening, an antecedent malignancy introduces the possibility of discovering metastasis as well as lung cancer. In some circumstances diagnosis and treatment of oligometastatic disease may confer a survival benefit. The survival benefit of treating either lung cancer or oligometastatic disease as result of lung cancer screening has yet to be determined. Further studies are needed to determine the role of lung cancer screening in the setting of previous malignancy.

• Lung cancer screening
• Criteria
• Previous malignancy
• Antecedent malignancy
• Lung metastasis
• Guidelines
• Head and neck cancer
• Lung cancer
• Low dose computed tomography
• Gastric cancer
• Breast cancer
• Colorectal cancer
• Lymphoma
• Esophageal cancer

Surveillance guidelines for breast cancer survivors recommend regular history and physical and mammography, and against routine imaging for detecting distant metastasis. Stage 0, I, II breast cancer cases treated at a major cancer center were identified from the Taiwan Cancer Registry. We used multivariable negative binomial and logistic regression analyses on institutional claims data to examine factors contributing to utilisation patterns of surveillance visits and tests in disease-free survivors. The mean number of surveillance visits during months 13 to 60 after cancer treatment initiation was 18.5 (SD 8.2) among the 2,090 breast cancer survivors followed for at least five years. After adjusting for patient and disease factors, the number of visits was the highest among patients mainly followed by medical oncologists compared to surgeons and radiation oncologists. Patient cohorts treated in more recent years had lower number of visits associated with care coordination effort, the adjusted mean being 19.2 visits for the 2002 cohort, and 16.3 visits for the 2008 cohort (p < 0.0001). Although imaging tests were highly utilised, there was a significant decrease in tumor marker testing from the 2002 to the 2008 treatment cohort (adjusted rate 99.4% to 35.1% respectively, p < 0.0001) in association with an institutional guideline change.