Elective Irradiation of Retropharyngeal Lymph Nodes as an Indication for Adjuvant Radiation Therapy After Transoral Surgery for Tonsil Cancer.

Treatment of squamous cell carcinoma of the tonsil involves primary radiation therapy (RT) or surgical resection. Historically, if RT was the primary or adjuvant treatment modality, most of the bilateral retropharyngeal lymph nodes (RPLNs) were treated electively with a therapeutic dose for subclinical disease, regardless of whether radiographically pathologic lymph nodes were seen on initial diagnostic imaging. De-escalation strategies include the incorporation of transoral surgery with the goal to either eliminate or reduce the dose of adjuvant RT or chemotherapy. Transoral surgery does not include elective removal of the RPLNs, and no guideline or outcome paper recommends adjuvant RT specifically to electively treat RPLNs. In this Topic Discussion, we discuss pertinent literature and suggest management decisions. The management decisions discussed in this Topic Discussion pertain to only tonsillar primaries and not those of the soft palate or base of the tongue.

A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients.

INTRODUCTION: Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP resolves or stabilizes without further treatment or a course of steroids, whereas true progression (TP) requires more aggressive management. Differentiating PsP from TP will affect the patient's outcome. This study investigated using deep learning to distinguish PsP MRI features from progressive disease. METHOD: We included GBM patients with a new or increasingly enhancing lesion within the original radiation field. We labeled those who subsequently were stable or improved on imaging and clinically as PsP and those with clinical and imaging deterioration as TP. A subset of subjects underwent a second resection. We labeled these subjects as PsP, or TP based on the histological diagnosis. We coregistered contrast-enhanced T1 MRIs with T2-weighted images for each patient and used them as input to a 3-D Densenet121 model and using five-fold cross-validation to predict TP vs PsP. RESULT: We included 124 patients who met the criteria, and of those, 63 were PsP and 61 were TP. We trained a deep learning model that achieved 76.4% (range 70-84%, SD 5.122) mean accuracy over the 5 folds, 0.7560 (range 0.6553-0.8535, SD 0.069) mean AUROCC, 88.72% (SD 6.86) mean sensitivity, and 62.05% (SD 9.11) mean specificity. CONCLUSION: We report the development of a deep learning model that distinguishes PsP from TP in GBM patients treated per the Stupp protocol. Further refinement and external validation are required prior to widespread adoption in clinical practice.

Vinculin-dependent actin bundling regulates cell migration and traction forces.

Vinculin binding to actin filaments is thought to be critical for force transduction within a cell, but direct experimental evidence to support this conclusion has been limited. In the present study, we found mutation (R1049E) of the vinculin tail impairs its ability to bind F-actin, stimulate actin polymerization, and bundle F-actin in vitro. Further, mutant (R1049E) vinculin expressing cells are altered in cell migration, which is accompanied by changes in cell adhesion, cell spreading and cell generation of traction forces, providing direct evidence for the critical role of vinculin in mechanotransduction at adhesion sites. Lastly, we discuss the viability of models detailing the F-actin-binding surface on vinculin in the context of our mutational analysis.