Dose distribution in the thyroid gland following radiation therapy of breast cancer-a retrospective study

<p>Abstract</p> <p>Purpose</p> <p>To relate the development of post-treatment hypothyroidism with the dose distribution within the thyroid gland in breast cancer (BC) patients treated with loco-regional radiotherapy (RT).</p> <p>Methods and materials</p> <p>In two groups of BC patients postoperatively irradiated by computer tomography (CT)-based RT, the individual dose distributions in the thyroid gland were compared with each other; Cases developed post-treatment hypothyroidism after multimodal treatment including 4-field RT technique. Matched patients in Controls remained free for hypothyroidism. Based on each patient's dose volume histogram (DVH) the volume percentages of the thyroid absorbing respectively 20, 30, 40 and 50 Gy were then estimated (V20, V30, V40 and V50) together with the individual mean thyroid dose over the whole gland (MeanTotGy). The mean and median thyroid dose for the included patients was about 30 Gy, subsequently the total volume of the thyroid gland (VolTotGy) and the absolute volumes (cm³) receiving respectively < 30 Gy and ≥ 30 Gy were calculated (Vol < 30 and Vol ≥ 30) and analyzed.</p> <p>Results</p> <p>No statistically significant inter-group differences were found between V20, V30, V40 and V50Gy or the median of MeanTotGy. The median VolTotGy in Controls was 2.3 times above VolTotGy in Cases (ρ = 0.003), with large inter-individual variations in both groups. The volume of the thyroid gland receiving < 30 Gy in Controls was almost 2.5 times greater than the comparable figure in Cases.</p> <p>Conclusions</p> <p>We concluded that in patients with small thyroid glands after loco-radiotherapy of BC, the risk of post-treatment hypothyroidism depends on the volume of the thyroid gland.</p

Weight Loss and Mortality in Overweight and Obese Cancer Survivors: A Systematic Review

Background Excess adiposity is a risk factor for poorer cancer survival, but there is uncertainty over whether losing weight reduces the risk. We conducted a critical review of the literature examining weight loss and mortality in overweight or obese cancer survivors. Methods We systematically searched PubMed and EMBASE for articles reporting associations between weight loss and mortality (cancer-specific or all-cause) in overweight/obese patients with obesity-related cancers. Where available, data from the same studies on non-overweight patients were compared. Results Five articles describing observational studies in breast cancer survivors were included. Four studies reported a positive association between weight loss and mortality in overweight/obese survivors, and the remaining study observed no significant association. Results were similar for non-overweight survivors. Quality assessment indicated high risk of bias across studies. Conclusions There is currently a lack of observational evidence that weight loss improves survival for overweight and obese cancer survivors. However, the potential for bias in these studies is considerable and the results likely reflect the consequences of disease-related rather than intentional weight loss. There is a need for stronger study designs, incorporating measures of intentionality of weight loss, and extended to other cancers

20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years

The administration of endocrine therapy for 5 years substantially reduces recurrence rates during and after treatment in women with early-stage, estrogen-receptor (ER)-positive breast cancer. Extending such therapy beyond 5 years offers further protection but has additional side effects. Obtaining data on the absolute risk of subsequent distant recurrence if therapy stops at 5 years could help determine whether to extend treatment

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs