Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets.

Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies.

Is radiomics a useful addition to magnetic resonance imaging in the preoperative classification of PitNETs?

Background: The WHO 2021 introduced the term pituitary neuroendocrine tumours (PitNETs) for pituitary adenomas and incorporated transcription factors for subtyping, prompting the need for fresh diagnostic methods. Current biomarkers struggle to distinguish between high- and low-risk non-functioning PitNETs. We explored if radiomics can enhance preoperative decision-making.

Sinonasal phosphaturic mesenchymal tumour: radiation oncologist's perspective.

Tumour-induced osteomalacia is a rare cause of osteomalacia, the majority of which is of mesenchymal origin. Oncogenic osteomalacia is a potentially curable condition caused by phosphaturic mesenchymal tumours. We present the case of a woman in her 30s with a sinonasal phosphaturic mesenchymal tumour, treated with surgical excision followed by adjuvant intensity-modulated radiotherapy and subsequent adjuvant chemotherapy.

Distinct Dysphagia Profiles in Patients With Oral Cancer After Surgery.

Objectives: To determine distinct profiles based on symptom severity in patients undergoing surgery for oral cancer and examine whether these profiles differ by participant characteristics.

Sample & Setting: 300 patients who underwent surgery for oral cancer at two outpatient clinics between June and December 2021.

Methods & Variables: Symptoms were assessed using the MD Anderson Symptom Inventory-Head and Neck Cancer Module.

Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization.

Background And Purpose: Radiomics models trained with limited single institution data are often not reproducible and generalisable. We developed radiomics models that predict loco-regional recurrence within two years of radiotherapy with private and public datasets and their combinations, to simulate small and multi-institutional studies and study the responsiveness of the models to feature selection, machine learning algorithms, centre-effect harmonization and increased dataset sizes.

Materials And Methods: 562 patients histologically confirmed and treated for locally advanced head-and-neck cancer (LA-HNC) from two public and two private datasets; one private dataset exclusively reserved for validation.