Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study

BACKGROUND: Planning radiosurgery to multiple intracranial metastases is complex and shows large variability in dosimetric quality among planners and treatment planning systems (TPS). This project aimed to determine whether autoplanning using the Muliple Brain Mets (AutoMBM) software can improve plan quality and reduce inter-planner variability by crowdsourcing results from prior international planning study. METHODS: Twenty-four institutions autoplanned with AutoMBM on a five metastases case from a prior international planning competition from which population statistics (means and variances) of 23 dosimetric metrics and resulting composite plan score (maximum score = 150) of other TPS (Eclipse, Monaco, RayStation, iPlan, GammaPlan, MultiPlan) were crowdsourced. Plan results of AutoMBM and each of the other TPS were compared using two sample t-tests for means and Levene's tests for variances. Plan quality of AutoMBM was correlated with the planner' experience and compared between academic and non-academic centers. RESULTS: AutoMBM produced plans with comparable composite plan score to GammaPlan, MultiPlan, Eclipse and iPlan (127.6 vs. 131.7 vs. 127.3 vs. 127.3 and 126.7; all p > 0.05) and superior to Monaco and RayStation (118.3 and 108.6; both p 0.05). CONCLUSIONS: By plan crowdsourcing prior international plan challenge, AutoMBM produces high and consistent plan quality independent of the planning experience and the institution that is crucial to addressing the technical bottleneck of SRS to intracranial metastases

Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study

Background: Planning radiosurgery to multiple intracranial metastases is complex and shows large variability in dosimetric quality among planners and treatment planning systems (TPS). This project aimed to determine whether autoplanning using the Muliple Brain Mets (AutoMBM) software can improve plan quality and reduce inter-planner variability by crowdsourcing results from prior international planning study. Methods: Twenty-four institutions autoplanned with AutoMBM on a five metastases case from a prior international planning competition from which population statistics (means and variances) of 23 dosimetric metrics and resulting composite plan score (maximum score = 150) of other TPS (Eclipse, Monaco, RayStation, iPlan, GammaPlan, MultiPlan) were crowdsourced. Plan results of AutoMBM and each of the other TPS were compared using two sample t-tests for means and Levene\u27s tests for variances. Plan quality of AutoMBM was correlated with the planner\u27 experience and compared between academic and non-academic centers. Results: AutoMBM produced plans with comparable composite plan score to GammaPlan, MultiPlan, Eclipse and iPlan (127.6 vs. 131.7 vs. 127.3 vs. 127.3 and 126.7; all p \u3e 0.05) and superior to Monaco and RayStation (118.3 and 108.6; both p \u3c 0.05). Inter-planner variability of overall plan quality was lowest for AutoMBM among all TPS (all p \u3c 0.05). AutoMBM\u27s plan quality did not differ between academic and non-academic centers and uncorrelated with planning experience (all p \u3e 0.05). Conclusions: By plan crowdsourcing prior international plan challenge, AutoMBM produces high and consistent plan quality independent of the planning experience and the institution that is crucial to addressing the technical bottleneck of SRS to intracranial metastases

Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics

Although next-generation sequencing (NGS) technology revolutionized sequencing, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, it continues to demonstrate serious limitations. In the early 2010s, the introduction of a novel set of sequencing methodologies, presented by two platforms, Pacific Biosciences (PacBio) and Oxford Nanopore Sequencing (ONT), gave birth to third-generation sequencing (TGS). The innovative long-read technologies turn genome sequencing into an ease-of-handle procedure by greatly reducing the average time of library construction workflows and simplifying the process of de novo genome assembly due to the generation of long reads. Long sequencing reads produced by both TGS methodologies have already facilitated the decipherment of transcriptional profiling since they enable the identification of full-length transcripts without the need for assembly or the use of sophisticated bioinformatics tools. Long-read technologies have also provided new insights into the field of epitranscriptomics, by allowing the direct detection of RNA modifications on native RNA molecules. This review highlights the advantageous features of the newly introduced TGS technologies, discusses their limitations and provides an in-depth comparison regarding their scientific background and available protocols as well as their potential utility in research and clinical applications

Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics

Although next-generation sequencing (NGS) technology revolutionized sequencing, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, it continues to demonstrate serious limitations. In the early 2010s, the introduction of a novel set of sequencing methodologies, presented by two platforms, Pacific Biosciences (PacBio) and Oxford Nanopore Sequencing (ONT), gave birth to third-generation sequencing (TGS). The innovative long-read technologies turn genome sequencing into an ease-of-handle procedure by greatly reducing the average time of library construction workflows and simplifying the process of de novo genome assembly due to the generation of long reads. Long sequencing reads produced by both TGS methodologies have already facilitated the decipherment of transcriptional profiling since they enable the identification of full-length transcripts without the need for assembly or the use of sophisticated bioinformatics tools. Long-read technologies have also provided new insights into the field of epitranscriptomics, by allowing the direct detection of RNA modifications on native RNA molecules. This review highlights the advantageous features of the newly introduced TGS technologies, discusses their limitations and provides an in-depth comparison regarding their scientific background and available protocols as well as their potential utility in research and clinical applications