Short‐term and long‐term clinical outcomes of uncommon types of invasive breast cancer

Yang, Mu; Bao, Wei; Zhang, Xinmin; Kang, Yibin; Haffty, Bruce G.; Zhang, Lanjing

doi:10.1111/his.13328

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…Second, we found downward trends in age-standardized 5-year net survival of some screened cancers, which are grade I and other histologic types. The downward trend in these screened breast cancers is concerning and warrants more studies, but consistent with a worse overall survival of other/uncommon type of breast cancers in the U.S. 32 Finally, patients with a black ethnical background in this study appeared not to have increasing proportional incidence of nonscreened breast cancer, nor (subsequent) steeper upward trend in net survival after 2007. However, the role of socioeconomic disparity/inequality in the screening uptake and survival of breast cancer remains controversial for English patients, including studies of supportive 33-36 and undermined conclusions.…”

Section: Discussionsupporting

confidence: 74%

Changing trends in proportional incidence and net survival of screened and nonscreened breast cancers among women in England

Wong

et al. 2019

Preprint

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Background: Uptake of breast cancer screening has been decreasing in England since 2007, and may increase proportional incidence of nonscreened cancers. However, recent trends in proportional incidence and net-survivals of screened and nonscreened breast cancers are unclear. Methods: We extracted population-based proportional incidence and age-standardized 5-year net-survivals from Public Health England, for English women with invasive breast cancer diagnosed during 1995-2011 (linked to death certificates, followed through 2016). Piecewise log-linear models with change-point/joinpoint were used to estimate temporal trends. We conducted a quasi-experimental study to test the hypothesis that the trend-change year of proportional incidence coincided with that of 5-year net-survival. Results: Among 254,063 women in England with invasive breast cancer diagnosed during 1995-2011, there was downward-to-upward trend-change in proportional incidence of nonscreened breast cancers (annual percent change[APC]=5.6 after 2007 versus APC=-3.5 before 2007, P<0.001) in diagnosis-year 2007, when steeper upward-trend in age-standardized 5-year net survival started (APC=5.7 after 2007/2008 versus APC=0.3 before 2007/2008, P<0.001).. Net-survival difference of screened versus nonscreened cancers also significantly narrowed (18% in 2007/2008 versus 5% in 2011). Similar associations were found in all strata of race, cancer stage, grade and histology, except in Black patients or patients with stage I, stage III, or grade I cancer. Conclusions: The downward-to-upward trend-change in proportional incidence of nonscreened breast cancers is associated with steeper upward-trend in age-standardized 5-year net survival among English women in recent years. Survival benefits of breast cancer screening appear decreasing in recent years. The data support reduction of breast cancer screening in some patients.

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Section: Discussionsupporting

confidence: 74%

Changing trends in proportional incidence and net survival of screened and nonscreened breast cancers among women in England

Wong

et al. 2019

Preprint

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“…We obtained individual-level data from the Surveillance, Epidemiology, and End Results-18 (SEER-18) (www.seer.cancer.gov) SEER*Stat Database with Treatment Data using SEER*Stat software (Surveillance Research Program, National Cancer Institute SEER*Stat software (seer.cancer.gov/seerstat) version <8.3.6>) as we did before [13][14][15]. SEER-18 is the largest SEER database including cases from 18 states and covering near 30% of the U.S. population.…”

Section: Discussionmentioning

confidence: 99%

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Deng

Huang

Yuan

et al. 2021

Laboratory Investigation

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Most of the biomedical datasets, including those of 'omics, population studies and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms.Machine learning (ML) algorithms have been used to classify outcomes in biomedical datasets, including random forests (RF), decision tree (DT), artificial neural networks (ANN) and support vector machine (SVM). However, their performance and efficiency in classifying multi-category outcomes in rectangular data are poorly understood. Therefore, we aimed to compare these metrics among the 4 ML algorithms. As an example, we created a large rectangular dataset using the female breast cancers in the Surveillance, Epidemiology, and End Results-18 (SEER-18) database which were diagnosed in 2004 and followed up until December 2016. The outcome was the 6-category cause of death, namely alive, non-breast cancer, breast cancer, cardiovascular disease, infection and other cause. We included 58 dichotomized features from ~53,000 patients. All analyses were performed using MatLab (version 2018a) and the 10-fold cross validation approach. The accuracy in classifying 6-category cause of death with DT, RF, ANN and SVM was 72.68%, 72.66%, 70.01% and 71.85%, respectively. Based on the information entropy and information gain of feature values, we optimized dimension reduction (i.e. reduce the number of features in models).We found 22 or more features were required to maintain the similar accuracy, while the running time decreased from 440s for 58 features to 90s for 22 features in RF, from 70s to 40s in ANN and from 440s to 80s in SVM. In summary, we here show that RF, DT, ANN and SVM had similar accuracy for classifying multi-category outcomes in this large rectangular dataset. Dimension reduction based on information gain will significantly increase model's efficiency while maintaining classification accuracy.

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“…In summary, no significant differences have been shown regarding OS but some decreases in in-breast recurrences after RT [27][28][29] as recent meta-analyses confirmed [30,31]. Interestingly, there is also some evidence that effects of RT also depend on histopathologic subtypes since RT did not improve OS in patients with invasive lobular or uncommon invasive breast cancer types [32].…”

Section: Plos Onementioning

confidence: 96%

Effect of adjuvant radiotherapy in elderly patients with breast cancer

et al. 2020

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Background Radiotherapy (RT) is of critical importance in the locoregional management of early breast cancer. Although RT is routinely used following breast conserving surgery (BCS), patients may occasionally be effectively treated with BCS alone. Currently, the selection of patients undergoing BCS who do not need breast irradiation is under investigation. With the advancement of personalized medicine, there is an increasing interest in reduction of aggressive treatments especially in older women. The primary objective of this study was to identify elderly patients who may forego breast irradiation after BCS without measurable consequences on local tumor growth and survival. Methods We analyzed 2384 early breast cancer patients aged 70 and older who were treated in 17 German certified breast cancer centers between 2001 and 2009. We compared RT versus no RT after guideline adherent (GA) BCS. The outcomes studied were breast cancer recurrence (RFS) and breast cancer-specific survival (BCSS). Low-risk patients were defined by luminal A, tumor size T1 or T2 and node-negative whereas higher-risk patients were defined by patients with G3 or T3/T4 or node-positive or other than Luminal A tumors. To test if there is a difference between two or more survival curves, we used the G p family of tests of Harrington and Fleming. Results The median age was 77 yrs (mean 77.6±5.6 y) and the median observation time 46 mths (mean 48.9±24.8 mths). 950 (39.8%) patients were low-risk and 1434 (60.2%) were higherrisk. 1298 (54.4%) patients received GA BCS of which 85.0% (1103) received GA-RT and only 15% (195) did not. For low-risk patients with GA-BCS there were no significant differences in RFS (log rank p = 0.651) and in BCSS (p = 0.573) stratified by GA-RT. 5 years RFS in both groups were > 97%. For higher-risk patients with GA-BCS we found a significant difference (p<0.001) in RFS and tumor-associated OS stratified by GA-RT. The results remain

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Short‐term and long‐term clinical outcomes of uncommon types of invasive breast cancer

Abstract: Uncommon IBCs have distinct patterns of prognosis and survival. The effectiveness of radiotherapy in women with uncommon IBC may differ by age. The underlying mechanisms warrant further studies.

Cited by 12 publications

References 34 publications

Changing trends in proportional incidence and net survival of screened and nonscreened breast cancers among women in England

Changing trends in proportional incidence and net survival of screened and nonscreened breast cancers among women in England

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Effect of adjuvant radiotherapy in elderly patients with breast cancer

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