Mesenchymal neoplasms with NTRK and other kinase gene alterations

Kinase alterations are increasingly recognised as oncogenic drivers in mesenchymal tumours. Infantile fibrosarcoma and the related renal tumour, congenital mesoblastic nephroma, were among the first solid tumours shown to harbour recurrent tyrosine kinase fusions, with the canonical ETV6::NTRK3 fusion identified more than 20 years ago. Although targeted testing has long been used in diagnosis, the advent of more robust sequencing techniques has driven the discovery of kinase alterations in an array of mesenchymal tumours. As our ability to identify these genetic alterations has improved, as has our recognition and understanding of the tumours that harbour these alterations. Specifically, this study will focus upon mesenchymal tumours harbouring NTRK or other kinase alterations, including tumours with an infantile fibrosarcoma-like appearance, spindle cell tumours resembling lipofibromatosis or peripheral nerve sheath tumours and those occurring in adults with a fibrosarcoma-like appearance. As publications describing the histology of these tumours increase so, too, do the variety kinase alterations reported, now including NTRK1/2/3, RET, MET, RAF1, BRAF, ALK, EGFR and ABL1 fusions or alterations. To date, these tumours appear locally aggressive and rarely metastatic, without a clear link between traditional features used in histological grading (e.g. mitotic activity, necrosis) and outcome. However, most of these tumours are amenable to new targeted therapies, making their recognition of both diagnostic and therapeutic import. The goal of this study is to review the clinicopathological features of tumours with NTRK and other tyrosine kinase alterations, discuss the most common differential diagnoses and provide recommendations for molecular confirmation with associated treatment implications.     

Skin regeneration,repair, and reconstruction: present and future

European Surgery - Large skin defects caused by trauma (e.g., burns) or due to other reasons (e.g., tumor-related skin resections) require sufficient skin replacement. The constant improvement of...     

Praxis der perioperativen Prävention von Phantomschmerz: eine deutschlandweite Umfrage

Die Anaesthesiologie - Phantomschmerzen haben eine hohe Prävalenz nach Majoramputationen und sind mit einer zusätzlichen Einschränkung der Lebensqualität verbunden....     

Standardisierte Kontrastmittelsonographie (CEUS) in der klinischen Akut- und Notfallmedizin sowie Intensivmedizin (CEUS-Akut)

Die Anaesthesiologie -     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.