Predicting leptomeningeal disease spread after resection of brain metastases using machine learning.

Objective: The incidence of leptomeningeal disease (LMD) has increased as treatments for brain metastases (BMs) have improved and patients with metastatic disease are living longer. Sample sizes of individual studies investigating LMD after surgery for BMs and its risk factors have been limited, ranging from 200 to 400 patients at risk for LMD, which only allows the use of conventional biostatistics. Here, the authors used machine learning techniques to enhance LMD prediction in a cohort of surgically treated BMs.

Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.

Background: Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain confined to in silico validation in small internal cohorts, without external validation or data on real-world clinical utility. We developed a strategy for the clinical validation of deep learning models for segmenting primary non-small-cell lung cancer (NSCLC) tumours and involved lymph nodes in CT images, which is a time-intensive step in radiation treatment planning, with large variability among experts.

Analysis of Spatial Dose-Volume Relationships and Decline in Sexual Function Following Permanent Brachytherapy for Prostate Cancer.

Objective: To explore relationships between dose to periprostatic anatomic structures and erectile dysfunction (ED) outcomes in an institutional cohort treated with prostate brachytherapy.

Methods: The Sexual Health Inventory for Men (SHIM) instrument was administered for stage cT1-T2 prostate cancer patients treated with Pd-103 brachytherapy over a 10-year interval. Dose volume histograms for regional organs at risk and periprostatic regions were calculated with and without expansions to account for contouring uncertainty.

Prospective evaluation of patient reported swallow function with the Functional Assessment of Cancer Therapy (FACT), MD Anderson Dysphagia Inventory (MDADI) and the Sydney Swallow Questionnaire (SSQ) in head and neck cancer patients.

Objectives: The Functional Assessment of Cancer Therapy (FACT) instrument is comprised of a group of related and overlapping quality of life (QoL) questionnaires including a core general form, head and neck cancer (HNC)-specific items, and an expert-selected index (FACT-HNSI). Understanding how these relate to more HNC-specific instruments such as the MD Anderson Dysphagia Inventory (MDADI) and Sydney Swallow Questionnaire (SSQ) is vital for guiding their use in clinical trials.

Materials And Methods: HNC patients concurrently completed MDADI, SSQ, and FACT questionnaires at radiation oncology clinic visits (2015-2016).

Adjuvant radiotherapy and outcomes of presumed hemorrhagic melanoma brain metastases without malignant cells.

Background: Patients with melanoma can present with a hemorrhagic intracranial lesion. Upon resection, pathology reports may not detect any malignant cells. However, the hemorrhage may obscure their presence and so physicians may still decide whether adjuvant radiotherapy should be applied.

Effects of perineural invasion on biochemical recurrence and prostate cancer-specific survival in patients treated with definitive external beam radiotherapy.

Objectives: Perineural invasion (PNI) has not yet gained universal acceptance as an independent predictor of adverse outcomes for prostate cancer treated with external beam radiotherapy (EBRT). We analyzed the prognostic influence of PNI for a large institutional cohort of prostate cancer patients who underwent EBRT with and without androgen deprivation therapy (ADT).

Material And Methods: We, retrospectively, reviewed prostate cancer patients treated with EBRT from 1993 to 2007 at our institution.

Surveillance, epidemiology, and end results-based analysis of the impact of preoperative or postoperative radiotherapy on survival outcomes for T3N0 rectal cancer.

Purpose: Preoperative chemoradiation has been established as standard of care for T3/T4 node-positive rectal cancer. Recent work, however, has called into question the overall benefit of radiation for tumors with lower risk characteristics, particularly T3N0 rectal cancers. We retrospectively analyzed T3N0 rectal cancer patients and examined how outcomes differed according to the sequence of treatment received.

An integrative probabilistic model for identification of structural variation in sequencing data.

Paired-end sequencing is a common approach for identifying structural variation (SV) in genomes. Discrepancies between the observed and expected alignments indicate potential SVs. Most SV detection algorithms use only one of the possible signals and ignore reads with multiple alignments.