Breast cancer remains the most common non-cutaneous malignancy in women in both Europe and the United States and the second leading cause of cancer-related deaths. In this breast cancer mortality and survival study, a US retrospective population-based analysis of 656,501 microscopically confirmed breast cancer cases, 1975-2019, data is derived from the NCI Surveillance Epidemiology & End Results Program, SEER*Stat 8.4.0.1.

Cancer of the tongue is an uncommon cancer site, with only 31,378 cases in the SEER 1975-2017 database, fewer than 1% of all reported cancers. This article updates trends in incidence, prevalence, short and long-term survival and mortality of tongue carcinoma.

During the past 5 decades, there have been reports of increases in the incidence and mortality rates of non-Hodgkin lymphoma (NHL) in the United States and globally. The ability to address the epidemiologic diversity, prognosis and treatment of NHL depends on the use of an accurate and consistent classification system. Historically, uniform treatment for NHL has been hampered by the lack of a systematic taxonomy of non-Hodgkin lymphoma. Before 1982, there were 6 competing classification schemes with contending terminologies for NHL: the Rappaport, Lukes-Collins, Kiel, World Health Organization, British, and Dorfman systems without consensus as to which system is most satisfactory regarding clinical relevance, scientific accuracy and reproducibility and presenting a difficult task for abstractors of incidence information. In 1982, the National Cancer Institute sponsored a workshop1 that developed a working formulation designed to: 1) provide clinicians with prognostic information for the various types of NHLs, and 2) provide a common language that might be used to compare clinical trials from various treatment centers around the world. Studies imply that prognosis is dependent on tumor stage and histology rather than the primary localization per se.2 This study utilizes the National Cancer Institute PDQ adaptation of the World Health Organization\'s (WHO) updated REAL (Revised European American Lymphoma) classification3 of lymphoproliferative diseases, and the SEER*Stat 8.3.6 database (released Aug 8, 2019) for diagnosis years 1975-2016. In this article, we make use of 40 years of data to examine patterns of incidence, survival and mortality, and selected cell bio-behavioral characteristics of NHL in the United States.
OBJECTIVE: -To update trends in incidence and prevalence in the United States of non-Hodgkin lymphoma, examine, compare and contrast short and long-term patterns of survival and mortality, and consider the outcome impacts of anatomic location of NHL nodal and extranodal subdivisions, utilizing selected ICD-O-3 histologic oncotypes stratified by age, sex, race/ethnicity, stage, cell behavioral morphology and histologic typology, cohort entry time-period and disease duration, employing the statistical database of the National Cancer Institute SEER*Stat 8.3.6 program for diagnosis years 1975-2016.4 Methods.- A retrospective, population-based cohort study using nationally representative data from the National Cancer Institute\'s (NCI) Surveillance, Epidemiology, and End Results (SEER) program to evaluate 384,651 NHL cases for diagnosis years 1975-2016 comparing multiple variables of age, sex, race, stage, cell behavioral morphology, cohort entry time-period, disease duration and histologic oncotype. Relative survival statistics were analyzed in two cohorts: 1975-1995 and 1996-2016. Survival statistics were derived from SEER*Stat Database: Incidence - SEER 9 Regs Research Data, November 2018 Submission (1975-2016) released April 2019, based on the November 2018 submission.
RESULTS: - Incidence rates, relative frequency distributions, survival and mortality by age, sex, stage and cell behavioral morphology, of adult nodal (N) and extranodal (EN) NHL in 2 entrant time-periods as recorded in the SEER Program of the National Cancer Institute for diagnosis years 1975-2016 (SEER Stat 8.3.6) are summarized. Shifts in trends over time are identified, and the findings are correlated with prognosis, including short and long-term observed (actual), expected and relative survival, median observed and relative survival, mortality rates and excess death rates per 1000 people.
CONCLUSIONS: - Trends in SEER incidence, prevalence, survival and mortality by age, sex, race, stage, cell behavioral morphology, cohort entry time-period, relative frequency and percent distribution, were examined to provide a current epidemiologic and medical-actuarial risk assessment framework for nodal (N) and extranodal (EN) non-Hodgkin\'s lymphoma in the 1975-2016 timeframe.

UNASSIGNED: For several cancers, including those of the breast, young age at diagnosis is associated with an adverse prognosis. Although this effect is often attributed to heritable mutations such as BRCA1/2, the relationship between pathologic features, young age of onset, and prognosis for breast cancer remains unclear. In the present study, we highlight links between age of onset and lymph node metastasis (NM) in US women with breast cancer.
UNASSIGNED: Case listings from Surveillance, Epidemiology, and End Result (SEER) 18 registry data for women with breast cancer, which include information on race, were used. NM and its associated outcomes were evaluated for a subset of women with receptor subtype information and then compared against a larger, pre-subtype validation set of data from the same registry. Age of diagnosis was a 5-category variable; under 40 years, 40-49 years, 50-59 years, 60-69 years and 70+ years. Univariate and adjusted multivariate survival models were applied to both sets of data.
UNASSIGNED: As determined with adjusted logistic regression models, women under 40 years old at diagnosis had 1.55 times the odds of NM as women 60-69 years of age. The odds of NM for (HR = hormone receptor) HR+/HER2+, HR-/HER2+, and triple-negative breast cancer subtypes were significantly lower than those for HR+/HER2-. In subtype-stratified adjusted models, age of diagnosis had a consistent trend of decreasing odds of NM by age category, most noticeable for HR+ subtypes of luminal A and B. Univariate 5-year survival by age was worst for women under 40 years, with NM attributable for 49% of the hazard of death from cancer in adjusted multivariate models.
UNASSIGNED: Lymph node metastasis is age-dependent, yet not all molecular subtypes are clearly affected by this relationship. For <40-yr-old women, NM is a major cause for shorter survival. When stratified by subtype, the strongest associations were in HR+ groups, suggesting a possible hormonal connection between young age of breast cancer onset and NM.

Survival period prediction through early diagnosis of cancer has many benefits. It allows both patients and caregivers to plan resources, time and intensity of care to provide the best possible treatment path for the patients. In this paper, by focusing on lung cancer patients, we build several survival prediction models using deep learning techniques to tackle both cancer survival classification and regression problems. We also conduct feature importance analysis to understand how lung cancer patients\' relevant factors impact their survival periods. We contribute to identifying an approach to estimate survivability that are commonly and practically appropriate for medical use.
We have compared the performance across three of the most popular deep learning architectures - Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) while comparing the performing of deep learning models against traditional machine learning models. The data was obtained from the lung cancer section of Surveillance, Epidemiology, and End Results (SEER) cancer registry.
The deep learning models outperformed traditional machine learning models across both classification and regression approaches. We obtained a best of 71.18 % accuracy for the classification approach when patients\' survival periods are segmented into classes of \'<=6 months\',\' 0.5 - 2 years\' and \'>2 years\' and Root Mean Squared Error (RMSE) of 13.5 % andR2 value of 0.5 for the regression approach for the deep learning models while the traditional machine learning models saturated at 61.12 % classification accuracy and 14.87 % RMSE in regression.
This approach can be a baseline for early prediction with predictions that can be further improved with more temporal treatment information collected from treated patients. In addition, we evaluated the feature importance to investigate the model interpretability, gaining further insight into the survival analysis models and the factors that are important in cancer survival period prediction.