A wide variety of fractionation schedules have been employed for the treatment of early glottic cancer. The aim is to report our 10-year experience of using hypofractionated radiotherapy with 55Gy in 20 fractions at 2.75Gy per fraction. Patients treated between 2004 and 2013 with definitive radiotherapy to a dose of 55Gy in 20 fractions over 4 weeks for T1/2 N0 squamous cell carcinoma of the glottis were retrospectively identified. Patients with prior therapeutic minor surgery (eg. laser stripping, cordotomy) were included. The probabilities of local control, ultimate local control (including salvage surgery), regional control, cause specific survival (CSS) and overall survival (OS) were calculated. One hundred thirty-two patients were identified. Median age was 65 years (range 33–89). Median follow up was 72 months (range 7–124). 50 (38 %), 18 (14 %) and 64 (48 %) of patients had T1a, T1b and T2 disease respectively. Five year local control and ultimate local control rates were: overall - 85.6 % and 97.3 % respectively, T1a - 91.8 % and 100 %, T1b - 81.6 and 93.8 %, and T2 - 80.9 % and 95.8 %. Five year regional control, CSS and OS rates were 95.4 %, 95.7 % and 78.8 % respectively. There were no significant associations of covariates (e.g. T-stage, extent of laryngeal extension, histological grade) with local control on univariate analysis. Only increasing age and transglottic extension in T2 disease were significantly associated with overall survival (both p <0.01). Second primary cancers developed in 17 % of patients. 13 (9.8 %) of patients required enteral tube feeding support during radiotherapy; no patients required long term enteral nutrition. One patient required a tracheostomy due to a non-functioning larynx on long term follow up. Hypofractionated radiation therapy with a dose of 55Gy in 20 fractions for early stage glottic cancer provides high rates of local control with acceptable toxicity.

Brain metastases present a significant complication in lung cancer with an unmet therapeutic need. In this single-centre, retrospective study, we genotyped a clinico-pathologically well-annotated cohort of consecutively resected brain metastases of lung adenocarcinomas and paired primary tumours, diagnosed from 2000 to 2015, using the Ion Torrent Oncomine Comprehensive Cancer Panel v3. Among 444 consecutive brain metastases, 210 (49%) originated from lung cancer. Analysis was successful in 111 samples, including 54 pairs of brain metastasis and primary tumour. Most driver alterations were preserved in brain metastases. Private alterations exclusive to primary tumours, brain metastases or both sites (intersecting cases) were present in 22%, 26% and 26% of cases, respectively. Seven percent had no shared mutations. KRAS mutations were more frequent in primary tumours metastasised to the brain (32/55, 58%) compared to TCGA (33%, p < 0.005) and own data from routine diagnostics, independent from clinical or pathological characteristics. Fourteen cases showed alterations in the EGFR signalling pathway including additional KRAS alterations that were private to brain metastases. KRAS G12C was detected most frequently (26% of patients) and KRAS G12C and G13C variants were significantly enriched in brain metastases. Synchronous and metachronous cases had a similar mutation profile. Our results suggest an important role of KRAS alterations in the pathobiology of brain metastases from lung adenocarcinomas. This has direct therapeutic implications as inhibitors selectively targeting KRAS G12C are entering the clinics.

Background and Purpose: Larynx cancer represents one of the most frequently diagnosed head and neck malignancies, which is most often confined to the glottic area. The aim of this study was to report the oncological outcome and identify prognostic factors in early-stage glottic squamous cell carcinoma treated with radiotherapy. Material and Methods: Patients (n = 761) diagnosed and treated in 10 centers between 1990 and 2015 were retrospectively analyzed. Probabilities of loco-regional control (LRC) and overall survival (OS) were calculated and possible prognostic factors were analyzed using Cox proportional hazards models. Results: The median follow-up was 63 months (range: 2–243). Three hundred and sixty-four, 148 and 249 patients had cT1a, cT1b, and cT2 stage I-II disease, respectively. Five and 10-years LRC/OS rates in the whole cohort were 83/82% and 80/68%, respectively. Three patients developed distant recurrences. In univariate analysis, male sex (HR: 3.49; 95% CI: 1.47–11.37; p < 0.01), T2 vs. T1a (HR: 1.62; 95% CI: 1.08–2.43; p = 0.02) and anterior commissure involvement (ACI) (HR: 1.66; 95% CI: 1.38–2.45; p < 0.01) were associated with impaired LRC. In multivariate analysis, male sex (HR: 3.42; 95% CI: 1.44–11.17; p < 0.01) and ACI (HR: 1.51; 95% CI: 1.01–2.28; p = 0.047) remained poor prognostic factors. No relation of treatment technique and biologically equivalent dose (BED) to oncological outcome was identified except for higher BED₁₀(L = 25; T = 1) yielding better LRC in T1a tumors (p = 0.04) in univariate analyses. Conclusion: Our results highlight the negative impact of ACI on tumor control. A less-expected finding was the impact of sex on tumor control. Further research is needed to validate its prognostic value and investigate any related biologic or behavioral factors, which may be modified to improve oncologic outcome.

Despite fast evolution cycles in deep learning methodologies for medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the lack of open-source frameworks feasible for processing DICOM RT Structure Sets. Besides this shortage, available open-source DICOM RT Structure Set converters rely exclusively on 2D reconstruction approaches leading to pixelated contours with potentially low acceptance by healthcare professionals. PyRaDiSe, an open-source, deep learning framework independent Python package, addresses these issues by providing a framework for building auto-segmentation solutions feasible to operate directly on DICOM data. In addition, PyRaDiSe provides profound DICOM RT Structure Set conversion and processing capabilities; thus, it applies also to auto-segmentation-related tasks, such as dataset construction for deep learning model training. The PyRaDiSe package follows a holistic approach and provides DICOM data handling, deep learning model inference, pre-processing, and post-processing functionalities. The DICOM data handling allows for highly automated and flexible handling of DICOM image series, DICOM RT Structure Sets, and DICOM registrations, including 2D-based and 3D-based conversion from and to DICOM RT Structure Sets. For deep learning model inference, extending given skeleton classes is straightforwardly achieved, allowing for employing any deep learning framework. Furthermore, a profound set of pre-processing and post-processing routines is included that incorporate partial invertibility for restoring spatial properties, such as image origin or orientation. The PyRaDiSe package, characterized by its flexibility and automated routines, allows for fast deployment and prototyping, reducing efforts for auto-segmentation pipeline implementation. Furthermore, while deep learning model inference is independent of the deep learning framework, it can easily be integrated into famous deep learning frameworks such as PyTorch or Tensorflow. The developed package has successfully demonstrated its capabilities in a research project at our institution for organs-at-risk segmentation in brain tumor patients. Furthermore, PyRaDiSe has shown its conversion performance for dataset construction. The PyRaDiSe package closes the gap between data science and clinical radiotherapy by enabling deep learning segmentation models to be easily transferred into clinical research practice. PyRaDiSe is available on https://github.com/ubern-mia/pyradise and can be installed directly from the Python Package Index using pip install pyradise.