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- Hedlund, P. O., et al.
(författare)
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Parenteral estrogen versus combined androgen deprivation in the treatment of metastatic prostatic cancer : Part 2. Final evaluation of the Scandinavian Prostatic Cancer Group (SPCG) Study No. 5
- 2008
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Ingår i: Scandinavian Journal of Urology and Nephrology. - : Informa UK Limited. - 0036-5599 .- 1651-2065. ; 42:3, s. 220-229
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Tidskriftsartikel (refereegranskat)abstract
- Objective. To compare parenteral estrogen therapy in the form of high-dose polyestradiol phosphate (PEP, Estradurin®) with combined androgen deprivation (CAD) in the treatment of prostate cancer patients with skeletal metastases. The aim of the study was to compare anticancer efficacy and adverse events, especially cardiovascular events. Material and methods. In total, 910 eligible patients with T0-4, NX, M1, G1-3 prostate cancer with an Eastern Cooperative Oncology Group performance status of 0-2 were randomized to treatment with either PEP 240mg i.m. twice a month for 2months and thereafter monthly, or flutamide (Eulexin®) 250mg t.i.d. per os in combination with either triptorelin (Decapeptyl®) 3.75mg i.m. per month or on an optional basis bilateral orchidectomy. Results. At this final evaluation of the trial 855 of the 910 patients were dead. There was no difference between the treatment groups in terms of biochemical or clinical progression-free survival or in overall or disease-specific survival. There was no difference in cardiovascular mortality, but a significant increase in non-fatal cardiovascular events in the PEP arm (p<0.05) predominantly caused by an increase in ischemic heart and heart decompensation events. There were 18 grave skeletal events in the CAD group but none in the PEP group (p=0.001). Conclusions. PEP has an anticancer efficacy equal to CAD and does not increase cardiovascular mortality in metastasized patients, but carries a significant risk of non-fatal cardiovascular events, which should be balanced against the skeletal complications in the CAD group. It is feasible to use Estradurin in the primary or secondary endocrine treatment of metastasized patients without prominent cardiac risk factors and especially those with osteoporosis. © 2008 Taylor & Francis.
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- Hagberg, Eva, et al.
(författare)
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Semi-supervised learning with natural language processing for right ventricle classification in echocardiography—a scalable approach
- 2022
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Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 143
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Tidskriftsartikel (refereegranskat)abstract
- We created a deep learning model, trained on text classified by natural language processing (NLP), to assess right ventricular (RV) size and function from echocardiographic images. We included 12,684 examinations with corresponding written reports for text classification. After manual annotation of 1489 reports, we trained an NLP model to classify the remaining 10,651 reports. A view classifier was developed to select the 4-chamber or RV-focused view from an echocardiographic examination (n = 539). The final models were two image classification models trained on the predicted labels from the combined manual annotation and NLP models and the corresponding echocardiographic view to assess RV function (training set n = 11,008) and size (training set n = 9951. The text classifier identified impaired RV function with 99% sensitivity and 98% specificity and RV enlargement with 98% sensitivity and 98% specificity. The view classification model identified the 4-chamber view with 92% accuracy and the RV-focused view with 73% accuracy. The image classification models identified impaired RV function with 93% sensitivity and 72% specificity and an enlarged RV with 80% sensitivity and 85% specificity; agreement with the written reports was substantial (both κ = 0.65). Our findings show that models for automatic image assessment can be trained to classify RV size and function by using model-annotated data from written echocardiography reports. This pipeline for auto-annotation of the echocardiographic images, using a NLP model with medical reports as input, can be used to train an image-assessment model without manual annotation of images and enables fast and inexpensive expansion of the training dataset when needed. © 2022
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