A comparison of cardiovascular and pulmonary morbidities and risk factors in breast cancer survivors compared to an age-matched female control group in the Lifelines prospective population cohort.

PURPOSE: To provide more insight into late treatment-related toxicities among breast cancer (BC) survivors by comparing morbidities and risk factors between BC survivors and age-matched controls. MATERIALS AND METHODS: All female participants diagnosed with BC before inclusion in Lifelines, a population-based cohort in the Netherlands, were selected and matched 1:4 to female controls without any oncological history on birth year. Baseline was defined as the age at BC diagnosis. Outcomes were obtained from questionnaires and functional analyses performed at entry to Lifelines (follow-up 1; FU1) and several years later (FU2). Cardiovascular and pulmonary events were defined as morbidities that were absent at baseline but present at FU1 or FU2. RESULTS: The study consisted of 1,325 BC survivors and 5,300 controls. The median period from baseline (i.e., BC treatment) to FU1 and FU2 was 7 and 10 years, respectively. Among BC survivors more events of heart failure (OR: 1.72 [1.10-2.68]) and less events of hypertension (OR: 0.79 [0.66-0.94]) were observed. At FU2, more electrocardiographic abnormalities were found among BC survivors compared to controls (4.1% vs. 2.7%, respectively; p = 0.027) and Framingham scores for the 10-year risk of coronary heart disease were lower (difference: 0.37%; 95% CI [-0.70 to -0.03%]). At FU2, BC survivors had more frequently a forced vital capacity below the lower limit of normal than controls (5.4% vs. 2.9%, respectively; p = 0.040). CONCLUSION: BC survivors are at risk of late treatment-related toxicities despite a more favourable cardiovascular risk profile compared to age-matched female controls.

Supraventricular cardiac conduction system exposure in breast cancer patients treated with radiotherapy and association with heart and cardiac chambers doses.

Purpose: To assess sinoatrial node (SAN) and atrioventricular node (AVN) doses for breast cancer (BC) patients treated with 3D-CRT and evaluate whether "large" cardiac structures (whole heart and four cardiac chambers) would be relevant surrogates. Material and methods: This single center study was based on 116 BCE patients (56 left-sided, 60 right-sided) treated with 3D-CRT without respiratory gating strategies and few IMN irradiations from 2009 to 2013. The heart, the left and right ventricles (LV, RV), the left and right atria (LA, RA) were contoured using multi-atlases for auto-segmentation. The SAN and the AVN were manually delineated using a specific atlas. Based on regression analysis, the coefficients of determination (R2) were estimated to evaluate whether "large" cardiac structures were relevant surrogates (R2 > 0.70) of SAN and AVN doses. Results: For left-sided BC, mean doses were: 3.60 ± 2.28 Gy for heart, 0.47 ± 0.24 Gy for SAN and 0.74 ± 0.29 Gy for AVN. For right-sided BC, mean heart dose was 0.60 ± 0.25 Gy, mean SAN dose was 1.57 ± 0.63 Gy (>85 % of patients with SAN doses > 1 Gy) and mean AVN dose was 0.51 ± 0.14 Gy. Among all "large" cardiac structures, RA appeared as the best surrogate for SAN doses (R2 > 0.80). Regarding AVN doses, the RA may also be an interesting surrogate for left-sided BC (R2 = 0.78), but none of "large" cardiac structures appeared as relevant surrogates among right-sided BC (all R2 < 0.70), except the LA for patients with IMN (R2 = 0.83). Conclusions: In BC patients treated 10 years ago with 3D-CRT, SAN and AVN exposure was moderate but could exceed 1 Gy to the SAN in many right-sided patients with no IMN-inclusion. The RA appeared as an interesting surrogate for SAN exposure. Specific conduction nodes delineation remains necessary by using modern radiotherapy techniques.

Automatic Cardiac Structure Contouring for Small Datasets with Cascaded Deep Learning Models.

Cardiac structure contouring is a time consuming and tedious manual activity used for radiotherapeutic dose toxicity planning. We developed an automatic cardiac structure segmentation pipeline for use in low-dose non-contrast planning CT based on deep learning algorithms for small datasets. Fifty CT scans were retrospectively selected and the whole heart, ventricles and atria were contoured. A two stage deep learning pipeline was trained on 41 non contrast planning CTs, tuned with 3 CT scans and validated on 6 CT scans. In the first stage, An InceptionResNetV2 network was used to identify the slices that contained cardiac structures. The second stage consisted of three deep learning models trained on the images containing cardiac structures to segment the structures. The three deep learning models predicted the segmentations/contours on axial, coronal and sagittal images and are combined to create the final prediction. The final accuracy of the pipeline was quantified on 6 volumes by calculating the Dice similarity coefficient (DC), 95% Hausdorff distance (95% HD) and volume ratios between predicted and ground truth volumes. Median DC and 95% HD of 0.96, 0.88, 0.92, 0.80 and 0.82, and 1.86, 2.98, 2.02, 6.16 and 6.46 were achieved for the whole heart, right and left ventricle, and right and left atria respectively. The median differences in volume were -4, -1, + 5, -16 and -20% for the whole heart, right and left ventricle, and right and left atria respectively. The automatic contouring pipeline achieves good results for whole heart and ventricles. Robust automatic contouring with deep learning methods seems viable for local centers with small datasets.

Qualitative Evaluation of Common Quantitative Metrics for Clinical Acceptance of Automatic Segmentation: a Case Study on Heart Contouring from CT Images by Deep Learning Algorithms.

Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep learning algorithms make the results of many papers difficult to interpret and compare. In this paper, a qualitative evaluation is done on five established metrics to assess whether their values correlate with clinical usability. A total of 377 CT volumes with heart delineations were randomly selected for training and evaluation. A deep learning algorithm was used to predict the contours of the heart. A total of 101 CT slices from the validation set with the predicted contours were shown to three experienced radiologists. They examined each slice independently whether they would accept or adjust the prediction and if there were (small) mistakes. For each slice, the scores of this qualitative evaluation were then compared with the Sørensen-Dice coefficient (DC), the Hausdorff distance (HD), pixel-wise accuracy, sensitivity and precision. The statistical analysis of the qualitative evaluation and metrics showed a significant correlation. Of the slices with a DC over 0.96 (N = 20) or a 95% HD under 5 voxels (N = 25), no slices were rejected by the readers. Contours with lower DC or higher HD were seen in both rejected and accepted contours. Qualitative evaluation shows that it is difficult to use common quantification metrics as indicator for use in clinic. We might need to change the reporting of quantitative metrics to better reflect clinical acceptance.

Radiation-induced fibrosis in the boost area after three-dimensional conformal radiotherapy with a simultaneous integrated boost technique for early-stage breast cancer: A multivariable prediction model.

BACKGROUND AND PURPOSE: To develop a multivariable prediction model for the risk of grade⩾2 fibrosis in the boost area after breast conserving surgery (BCS) followed by three-dimensional conformal radiotherapy (RT) with a simultaneous integrated photon boost (3D-CRT-SIB), five years after RT. MATERIAL AND METHODS: This prospective cohort study included 1,030 patients treated with RT for breast cancer (stage 0-III), after BCS. Data regarding physician-rated fibrosis and dose-volume parameters were available in 546 patients. A multivariable logistic regression model for grade⩾2 fibrosis was generated. RESULTS: At 5years, grade⩾2 fibrosis was observed in 13.4% of the patients. The multivariable analysis resulted in a prediction model for grade⩾2 fibrosis in the boost area including three independent variables: patient age, breast volume receiving⩾55Gy (V55 CTV breast) and the maximum radiation dose in the breast (Dmax). CONCLUSIONS: A multivariable prediction model was developed including age, V55 CTV breast and Dmax for grade⩾2 fibrosis in the boost area after breast cancer RT using a 3D-CRT-SIB technique. This model can be used to estimate the risk of fibrosis and to optimize dose distributions aiming at reducing this risk.

Modest effect of p53, EGFR and HER-2/neu on prognosis in epithelial ovarian cancer: a meta-analysis.

BACKGROUND: P53, EGFR and HER-2/neu are the most frequently studied molecular biological parameters in epithelial ovarian cancer, but their prognostic impact is still unequivocal. We performed a meta-analysis to more precisely estimate their prognostic significance. METHODS: Published studies that investigated the association between p53, EGFR and HER-2/neu status and survival were identified. Meta-analysis was performed using a DerSimonian-Laird model. Publication bias was investigated using funnel plots and sources of heterogeneity were identified using meta-regression analysis. RESULTS: A total of 62 studies were included for p53, 15 for EGFR and 20 for HER-2/neu. P53, EGFR and HER-2/neu status had a modest effect on overall survival (pooled HR 1.47, 95% CI 1.33-1.61 for p53; HR 1.65, 95% CI 1.25-2.19 for EGFR and HR 1.67, 95% CI 1.34-2.08 for HER-2/neu). Meta-regression analysis for p53 showed that FIGO stage distribution influenced study outcome. For EGFR and HER-2/neu, considerable publication bias was present. CONCLUSIONS: Although p53, EGFR and HER-2/neu status modestly influences survival, these markers are, by themselves, unlikely to be useful as prognostic markers in clinical practice. Our study highlights the need for well-defined, prospective clinical trials and more complete reporting of results of prognostic factor studies.

The ErbB signalling pathway: protein expression and prognostic value in epithelial ovarian cancer.

Ovarian cancer is the most frequent cause of death from gynaecological cancer in the Western world. Current prognostic factors do not allow reliable prediction of response to chemotherapy and survival for individual ovarian cancer patients. Epidermal growth factor receptor (EGFR) and HER-2/neu are frequently expressed in ovarian cancer but their prognostic value remains unclear. In this study, we investigated the expression and prognostic value of EGFR, EGFR variant III (EGFRvIII), HER-2/neu and important downstream signalling components in a large series of epithelial ovarian cancer patients. Immunohistochemical staining of EGFR, pEGFR, EGFRvIII, Her-2/neu, PTEN (phosphatase and tensin homologue deleted on chromosome 10), total and phosphorylated AKT (pAKT) and phosphorylated ERK (pERK) was performed in 232 primary tumours using the tissue microarray platform and related to clinicopathological characteristics and survival. In addition, EGFRvIII expression was determined in 45 tumours by RT-PCR. Our results show that negative PTEN immunostaining was associated with stage I/II disease (P=0.006), non-serous tumour type (P=0.042) and in multivariate analysis with a longer progression-free survival (P=0.015). Negative PTEN staining also predicted improved progression-free survival in patients with grade III or undifferentiated serous carcinomas (P=0.011). Positive pAKT staining was associated with advanced-stage disease (P=0.006). Other proteins were expressed only at low levels, and were not associated with any clinicopathological parameter or survival. None of the tumours were positive for EGFRvIII. In conclusion, our results indicate that tumours showing negative PTEN staining could represent a subgroup of ovarian carcinomas with a relatively favourable prognosis.

MEIS and PBX homeobox proteins in ovarian cancer.

Three amino-acid loop extension (TALE) homeobox proteins MEIS and PBX are cofactors for HOX-class homeobox proteins, which control growth and differentiation during embryogenesis and homeostasis. We showed that MEIS and PBX expression are related to cisplatin resistance in ovarian cancer cell lines. Therefore, MEIS1, MEIS2 and PBX expression were investigated immunohistochemically in a tissue microarray (N=232) of ovarian cancers and ovarian surface epithelium (N=15). Results were related to clinicopathologic characteristics and survival. All cancers expressed MEIS1, MEIS2 and PBX in nucleus and cytoplasm. MEIS1 and 2 only stained nuclear in surface epithelium. Nuclear MEIS2 was negatively related to stage, grade and overall survival in univariate analyses. Additionally, MEIS and PBX RNA expression in ovarian surface epithelium and other normal tissues and ovarian cancer versus other tumour types using public array data sets were studied. In ovarian cancer, MEIS1 is highly expressed compared to other cancer types. In conclusion, MEIS and PBX are extensively expressed in ovarian carcinomas and may play a role in ovarian carcinogenesis.

Factors influencing p53 expression in ovarian cancer as a biomarker of clinical outcome in multicentre studies.

The prognostic impact of p53 immunostaining in a large series of tumours from epithelial ovarian cancer patients in a two-centre study was analysed. The study population (n=476) comprised of a retrospective series of 188 patients (Dutch cohort) and a prospective series of 288 patients (Scottish cohort) enrolled in clinical trials. P53 expression was determined by immunohistochemistry on tissue microarrays. Association with progression-free survival (PFS) and overall survival (OS) was analysed by univariate and multivariate Cox regression analysis. Aberrant p53 overexpression was significantly associated with PFS in the Dutch and Scottish cohorts (P=0.001 and 0.038, respectively), but not with OS in univariate analysis. In multivariate analysis, when the two groups were combined and account taken of clinical factors and country of origin of the cohort, p53 expression was not an independent prognostic predictor of PFS or OS. In this well-powered study with minimal methodological variability, p53 immunostaining is not an independent prognostic marker of clinical outcome in epithelial ovarian cancer. The data demonstrate the importance of methodological standardisation, particularly defining patient characteristics and survival end-point data, if biomarker data from multicentre studies are to be combined.

Molecular prognostic markers in ovarian cancer: toward patient-tailored therapy.

In ovarian cancer the ceiling seems to be reached with chemotherapeutic drugs. Therefore a paradigm shift is needed. Instead of treating all patients according to standard guidelines, individualized molecular targeted treatment should be aimed for. This means that molecular profiles of the distinct ovarian cancer subtypes should be established. Until recently, most studies trying to identify molecular targets were single-marker studies. The prognostic role of key components of apoptotic and prosurvival pathways such as p53, EGFR, and HER2 has been extensively studied because resistance to chemotherapy is often caused by failure of tumor cells to go into apoptosis. However, it is more than likely that different ovarian cancer subtypes with extensive molecular heterogeneity exist. Therefore, exploration of the potential of specific tumor-targeted therapy, based on expression of a prognostic tumor profile, may be of interest. Recently, new profiling techniques, such as DNA and protein microarrays, have enabled high-throughput screening of tumors. In this review an overview of the current status of prognostic marker and molecular targeting research in ovarian cancer, including microarray studies, is presented.

[Acute hepatitis after use of a herbal preparation with greater celandine (Chelidonium majus)]. / Acute hepatitis na gebruik van een plantaardig preparaat met stinkende gouwe (Chelidonium majus).

A 42-year-old woman developed jaundice due to acute hepatitis several weeks after ingestion of a herbal preparation containing greater celandine (Chelidonium majus) and curcuma root, which had been prescribed by an alternative therapist due to a skin complaint. After the medication had been withdrawn, clinical recovery was rapid and the hepatic functions returned to normal within 2 months. The hepatitis was ascribed to the known hepatotoxic effects of C. majus. In view of the increasing popularity of herbal remedies, greater awareness of side effects, such as hepatotoxicity, is needed. Quite a number of herbal preparations carry the risk of liver damage. The supposed clinical effectiveness of herbal remedies does not seem to always outweigh the potential risks. In the event of non-clarified liver function disturbances the ingestion of supposedly harmless, but potentially hepatotoxic, herbal products should be considered.