Długotrwała skuteczność sorafenibu u chorego na raka wątrobowokomórkowego ze współistnieniem hemochromatozy

W literaturze można znaleźć tylko jeden opis przypadku marskości wątroby u chorego z rakiem wątrobowokomórkowym (HCC, hepatocellular carcinoma) ze współistniejącą hemochromatozą dziedziczną, u którego przez 6 miesięcy stosowano leczenie sorafenibem. W niniejszej pracy przedstawiono przypadek chorego, pierwotnie bez marskości wątroby, u którego przypadkowo wykryto hemochromatozę w trakcie długotrwałej terapii sorafenibem z powodu HCC. Mężczyznę w wieku 53 lat z zaawansowanym HCC, u którego w momencie rozpoznania choroby nowotworowej nie stwierdzono marskości wątroby, leczono sorafenibem w dawce 400 mg dwa razy na dobę od października 2010 r. po przezskórnej ablacji największego guzka w wątrobie. Uzyskano ujemne wyniki badań serologicznych w kierunku wirusowego zapalenia wątroby typu B i typu C. Po dwóch latach terapii inhibitorem kinaz w kolejnym badaniu obrazowym stwierdzono zmiany w wątrobie o cechach marskości odpowiadające hemochromatozie. Rozpoznanie choroby związanej ze spichrzaniem żelaza potwierdzono na podstawie badania ekspresji genów — wykazano homozygotyczność genu HFE w odniesieniu do mutacji C282Y. Utrzymano sta­bilizację choroby nowotworowej, kontynuując stosowanie sorafenibu w stałej dawce standardowej przez 6 lat. Do chwili obecnej u chorego wykonano 35 flebotomii

Long-term response of hepatocellular carcinoma to sorafenib in a patient with HFE-haemochromatosis

In the literature there has been only one case report of cirrhotic patient with hepatocellular carcinoma (HCC) and with a history of hereditary haemochromatosis treated with sorafenib for six months. Herein, we describe a case of a primary non-cirrhotic patient who was incidentally diagnosed with haemochromatosis during prolonged therapy with sorafenib due to HCC. A 53-year-old primary non-cirrhotic man with advanced HCC was treated with sorafenib at 400 mg twice daily since October 2010 following percutaneous ablation treatment of the largest liver nodule. He was seronegative for hepatitis B and C virus. After two years of kinase inhibitor therapy, the liver changes with cirrhotic features suggesting hemochromatosis were discovered on repeated imaging. The diagnosis of associated iron-overload disease was confirmed by genotypic expression — he was homozygous for the HFE gene C282Y mutation. Maintaining cancer stabilisation by continuing sorafenib therapy at the fixed standard dose for six years, he has undergone thirty-five phlebotomies until now

Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires

Abstract Background Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and fertility issues. While numerous factors associated with endometriosis are documented, a wide range of symptoms may still be undiscovered. Methods In this study, we employed machine learning algorithms to predict endometriosis based on the patient symptoms extracted from 13,933 questionnaires. We compared the results of feature selection obtained from various algorithms (i.e., Boruta algorithm, Recursive Feature Selection) with experts’ decisions. As a benchmark model architecture, we utilized a LightGBM algorithm, along with Multivariate Imputation by Chained Equations (MICE) and k-nearest neighbors (KNN), for missing data imputation. Our primary objective was to assess the model’s performance and feature importance compared to existing studies. Results We identified the top 20 predictors of endometriosis, uncovering previously overlooked features such as Cesarean section, ovarian cysts, and hernia. Notably, the model’s performance metrics were maximized when utilizing a combination of multiple feature selection methods. Specifically, the final model achieved an area under the receiver operator characteristic curve (AUC) of 0.85 on the training dataset and an AUC of 0.82 on the testing dataset. Conclusions The application of machine learning in diagnosing endometriosis has the potential to significantly impact clinical practice, streamlining the diagnostic process and enhancing efficiency. Our questionnaire-based prediction approach empowers individuals with endometriosis to proactively identify potential symptoms, facilitating informed discussions with healthcare professionals about diagnosis and treatment options

Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.

Controlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient's genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched to groups of various MII oocyte counts using ranking, correspondence analysis, and self-organizing map methods. The gradient boosting machine technique was used to train models on a clinical dataset of 8,574 or a clinical-genetic dataset of 516 ovarian stimulations. The clinical-genetic model predicted the number of MII oocytes better than that based on clinical data. Anti-Müllerian hormone level and antral follicle count were the two most important predictors while a genetic feature consisting of sequence variants in the GDF9, LHCGR, FSHB, ESR1, and ESR2 genes was the third. The combined contribution of genetic features important for the prediction was over one-third of that revealed for anti-Müllerian hormone. Predictions of our clinical-genetic model accurately matched individuals' actual outcomes preventing over- or underestimation. The genetic data upgrades the personalized prediction of ovarian stimulation outcomes, thus improving the in vitro fertilization procedure