Neratinib as Extended Adjuvant Treatment of HER2-Positive/HR-Positive Early Breast Cancer Patients in Germany, Austria, and Switzerland: Interim Results of the Prospective, Observational ELEANOR Study.

Prognosis of patients diagnosed with HER2+ early breast cancer (eBC) has substantially improved, but distant recurrences impacting quality of life and survival still occur. One treatment option for extended adjuvant treatment of patients with HER2+/HR+ eBC is neratinib, available in Europe for patients who completed adjuvant trastuzumab-based therapy within 1 year. The ELEANOR study is investigating the real-world use of neratinib in Germany, Austria, and Switzerland. Results from an interim analysis of the first 200 patients observed for ≥3 months are reported. The primary objective of this prospective, multicenter, observational study is to assess patient adherence to neratinib (defined as the percentage of patients taking neratinib on ≥75% prescribed days). Secondary objectives are patient characteristics and treatment outcomes. At cut-off (May 2, 2022), a total of 202 patients had been observed for ≥3 months, with neratinib treatment documented for 187 patients (median age: 53.0 years; 67.9% at increased risk of disease recurrence). In total, 151 (80.7%) patients had received prior neoadjuvant treatment; of these, 82 (54.3%) patients achieved a pathologically complete response. Neratinib was initiated at a median 3.6 months after trastuzumab-based treatment, with 36.4% starting at a dose <240 mg/day. Treatment is ongoing for 46.0% of patients, with median treatment duration of 11.2 (interquartile range 0.9-12.0) months. Diarrhea was the most common adverse event (78.6% any grade, 20.3% grade ≥3); pharmacologic prophylaxis was used in 85.6% of patients. The pattern of anti-HER2 pretreatment observed reflected the current treatment for HER2+/HR+ eBC in Germany, Austria, and Switzerland. These interim results suggest that neratinib as an extended adjuvant is a feasible option after various anti-HER2 pretreatments and that its tolerability can be managed and improved with proactive diarrhea management

Radiomics biopsy signature for predicting survival in patients with spinal bone metastases (SBMs)

STUDY DESIGN: Retrospective analysis of a registered cohort of patients treated and irradiated for metastases in the spinal column in a single institute. OBJECTIVE: This is the first study to develop and internally validate radiomics features for predicting six-month survival probability for patients with spinal bone metastases (SBM). BACKGROUND DATA: Extracted radiomics features from routine clinical CT images can be used to identify textural and intensity-based features unperceivable to human observers and associate them with a patient survival probability or disease progression. METHODS: A study was conducted on 250 patients treated for metastases in the spinal column irradiated for the first time between 2014 and 2016, at the MAASTRO clinic in Maastricht, the Netherlands. The first 150 available patients were used to develop the model and the subsequent 100 patient were considered as a test set for the model. A bootstrap (B = 400) stepwise model selection, which combines both the forward and backward variable elimination procedure, was used to select the most useful predictive features from the training data based on the Akaike information criterion (AIC). The stepwise selection procedure was applied to the 400 bootstrap samples, and the results were plotted as a histogram to visualize how often each variable was selected. Only variables selected more than 90 % of the time over the bootstrap runs were used to build the final model. A prognostic index (PI) called radiomics score (radscore) and clinical score (clinscore) was calculated for each patient. The prognostic index was not scaled, the original values were used which can be extracted from the model directly or calculated as a linear combination of the variables in the model multiplied by the respective beta value for each patient. RESULTS: The clinical model had a good discrimination power. The radiomics model, on the other hand, had an inferior performance with no added predictive power to the clinical model. The internal imaging characteristics do not seem to have a value in the prediction of survival. However, the Shape features were excluded from further analyses in our study since all biopsies had a standard shape hence no variability

Neuro-/Polytrauma - Region Koeln. Teilprojekt 2 Vergleichende Untersuchung des Heilungsverlaufs nach schweren Schaedel-Hirn-Verletzungen bei Anwendung eines integrativen Versorgungskonzeptes mit fruehrehabilitativer und neuropsychologischer Fruehbehandlung gegenueber den bisherigen Behandlungsmassnahmen. Schlussbericht

Available from TIB Hannover: F99B1533 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Bildung und Forschung (BMBF), Bonn (Germany)DEGerman

Radiomics biopsy signature for predicting survival in patients with spinal bone metastases (SBMs)

Study design: Retrospective analysis of a registered cohort of patients treated and irradiated for metastases in the spinal column in a single institute. Objective: This is the first study to develop and internally validate radiomics features for predicting six-month survival probability for patients with spinal bone metastases (SBM). Background data: Extracted radiomics features from routine clinical CT images can be used to identify textural and intensity-based features unperceivable to human observers and associate them with a patient survival probability or disease progression. Methods: A study was conducted on 250 patients treated for metastases in the spinal column irradiated for the first time between 2014 and 2016, at the MAASTRO clinic in Maastricht, the Netherlands. The first 150 available patients were used to develop the model and the subsequent 100 patient were considered as a test set for the model. A bootstrap (B = 400) stepwise model selection, which combines both the forward and backward variable elimination procedure, was used to select the most useful predictive features from the training data based on the Akaike information criterion (AIC). The stepwise selection procedure was applied to the 400 bootstrap samples, and the results were plotted as a histogram to visualize how often each variable was selected. Only variables selected more than 90 % of the time over the bootstrap runs were used to build the final model.A prognostic index (PI) called radiomics score (radscore) and clinical score (clinscore) was calculated for each patient. The prognostic index was not scaled, the original values were used which can be extracted from the model directly or calculated as a linear combination of the variables in the model multiplied by the respective beta value for each patient. Results: The clinical model had a good discrimination power. The radiomics model, on the other hand, had an inferior performance with no added predictive power to the clinical model. The internal imaging characteristics do not seem to have a value in the prediction of survival. However, the Shape features were excluded from further analyses in our study since all biopsies had a standard shape hence no variability