Organ-at-risk dose prediction using a machine learning algorithm: Clinical validation and treatment planning benefit for lung SBRT.

Objective: To quantify the clinical performance of a machine learning (ML) algorithm for organ-at-risk (OAR) dose prediction for lung stereotactic body radiation therapy (SBRT) and estimate the treatment planning benefit from having upfront access to these dose predictions.

Methods: ML models were trained using multi-center data consisting of 209 patients previously treated with lung SBRT. Two prescription levels were investigated, 50 Gy in five fractions and 54 Gy in three fractions.

Oleaginous yeast: a value-added platform for renewable oils.

Yeast single cell oil (SCO) is a non-crop-based, renewable oil source that can be used for the production of bio-based oleochemicals. Stand-alone production of SCO for oleochemicals is currently not cost-competitive because lower-cost alternatives from petroleum and crop-based resources are available. Utilizing low-valued nutrient sources, implementing cost-efficient downstream processes and adopting biotechnological advancements such as systems biology and metabolic engineering could prove valuable in making an SCO platform a reality in the emerging bio-based economy.