Clinical relevance of circulating tumour cells in the bone marrow of patients with SCCHN

Background: Clinical outcome of patients with head and neck squamous cell carcinoma (SCCHN) depends on several risk factors like the presence of locoregional lymph node or distant metastases, stage, localisation and histologic differentiation of the tumour. Circulating tumour cells in the bone marrow indicate a poor prognosis for patients with various kinds of malignoma. The present study examines the clinical relevance of occult tumour cells in patients suffering from SCCHN. Patients and Methods: Bone marrow aspirates of 176 patients suffering from SCCHN were obtained prior to surgery and stained for the presence of disseminated tumour cells. Antibodies for cytokeratin 19 were used for immunohistochemical detection with APAAP on cytospin slides. Within a clinical follow-up protocol over a period of 60 months, the prognostic relevance of several clinicopathological parameters and occult tumour cells was evaluated. Results: Single CK19-expressing tumour cells could be detected in the bone marrow of 30.7% of the patients. There is a significant correlation between occult tumour cells in the bone marrow and relapse. Uni- and multivariate analysis of all clinical data showed the metastases in the locoregional lymph system and detection of disseminated tumour cells in the bone marrow to be statistically highly significant for clinical prognosis. Conclusion: The detection of minimal residual disease underlines the understanding of SCCHN as a systemic disease. Further examination of such cells will lead to a better understanding of the tumour biology, as well as to improvement of diagnostic and therapeutic strategies

Stereotactic arrhythmia radioablation: competitor or adjunct to catheter ablation?

Cardiology and Radiation Oncology working together—a new ‘STAR’ on the horizon? Until recently, most cardiologists associated radiation exposure to the heart with potential adverse effects, such as pericarditis, late coronary artery disease or potential damage to cardiac implantable devices. The landmark publication of 2017 reporting a case series of just five patients with recurrent ventricular tachycardia (VT) treated with stereotactic arrhythmia radioablation (STAR) changed this perception and introduced a new area for both cardiac electrophysiology and radiation oncology

Stereotaktische Bestrahlung: Lokale Tumorkontrolle enorm verbessert

Mit der stereotaktischen Strahlentherapie lässt sich die Bestrahlung so präzise auf das Zielvolumen richten, dass die Dosis und damit der Antitumoreffekt erhöht werden können. Lungenkrebs ist ein Beispiel für den klinischen Nutzen

Diagnosis and Treatment of Peripheral and Cranial Nerve Tumors with Expert Recommendations: An EUropean Network for RAre CANcers (EURACAN) Initiative

The 2021 WHO classification of the CNS Tumors identifies as "Peripheral nerve sheath tumors" (PNST) some entities with specific clinical and anatomical characteristics, histological and molecular markers, imaging findings, and aggressiveness. The Task Force has reviewed the evidence of diagnostic and therapeutic interventions, which is particularly low due to the rarity, and drawn recommendations accordingly. Tumor diagnosis is primarily based on hematoxylin and eosin-stained sections and immunohistochemistry. Molecular analysis is not essential to establish the histological nature of these tumors, although genetic analyses on DNA extracted from PNST (neurofibromas/schwannomas) is required to diagnose mosaic forms of NF1 and SPS. MRI is the gold-standard to delineate the extension with respect to adjacent structures. Gross-total resection is the first choice, and can be curative in benign lesions; however, the extent of resection must be balanced with preservation of nerve functioning. Radiotherapy can be omitted in benign tumors after complete resection and in NF-related tumors, due to the theoretic risk of secondary malignancies in a tumor-suppressor syndrome. Systemic therapy should be considered in incomplete resected plexiform neurofibromas/MPNSTs. MEK inhibitor selumetinib can be used in NF1 children ≥2 years with inoperable/symptomatic plexiform neurofibromas, while anthracycline-based treatment is the first choice for unresectable/locally advanced/metastatic MPNST. Clinical trials on other MEK1-2 inhibitors alone or in combination with mTOR inhibitors are under investigation in plexiform neurofibromas and MPNST, respectively

How we treat patients with leptomeningeal metastases

The goal of treatment of leptomeningeal metastasis is to improve survival and to maintain quality of life by delaying neurological deterioration. Tumour-specific therapeutic options in

Pretreatment 18F-FAZA PET Predicts Success of Hypoxia-Directed Radiochemotherapy Using Tirapazamine

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Stereotactic body radiotherapy to defer systemic therapy in patients with oligorecurrent disease

Background Patients who develop oligorecurrent disease may be treated with metastasis-directed stereotactic body radiotherapy (SBRT) to defer the start of systemic therapy and delay its potential side effects. We report oncological outcomes and patterns of failure in patients with oligorecurrent disease treated with SBRT and determine which factors impact the interval to initiation of systemic therapy. Material/Methods This retrospective study included patients with oligorecurrent disease (≤5 lesions) from any solid organ malignancy, treated with SBRT to all metastases and no systemic therapy for a minimum one month after SBRT between 01/2014 and 12/2019. The Kaplan-Meier method was used to analyze overall survival (OS) and progression-free survival (PFS), and the cumulative incidence of initiation of systemic therapy was analyzed assuming death without systemic therapy as a competing risk. Univariable and multivariable analyses are used to assess predictors of the systemic therapy-free interval. Results Among 545 patients treated with SBRT for oligometastatic disease, 142 patients were treated with SBRT only for oligorecurrent disease. The most common primary tumors were lung and gastrointestinal cancer in 47 (33.1 %) and 28 (19.7 %) patients, respectively. After a median follow-up of 25 months, the median PFS and OS was 6.1 months and 48.9 months, respectively. Distant metastases were the most common first failure, and oligometastatic distant failure occured in 86 patients (60.6 %). New metastases were treated with repeat SBRT in 48 patients (33.8 %). The 1- and 2-year cumulative incidence of initiation of systemic therapy was 24.6 % and 36.8 %, respectively. In multivariable analysis, the number of previous lines of systemic therapy and the cumulative volume of metastases were significantly associated with the interval to initiation of systemic therapy. Conclusion Selected patients with oligorecurrence achieved favorable OS and low cumulative incidence of initiation of systemic therapy. Prospective studies are warranted to determine how the deferral of systemic therapy impacts OS compared with immediate systemic therapy in combination with SBRT

Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model’s robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process

Deep-Learning-Based Dose Predictor for Glioblastoma-Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process