Pinheiro‐Neto, Carlos

AbstractBackgroundPatients with sinonasal malignancy (SNM) present with significant sinonasal quality of life (QOL) impairment. Global sinonasal QOL as measured by the 22‐item Sinonasal Outcomes Test (SNOT‐22) has been shown to improve with treatment. This study aims to characterize SNOT‐22 subdomain outcomes in SNM.MethodsPatients diagnosed with SNM were prospectively enrolled in a multi‐center patient registry. SNOT‐22 scores were collected at the time of diagnosis and through the post‐treatment period for up to 5 years. Multivariable regression analysis was used to identify drivers of variation in SNOT‐22 subdomains.ResultsNote that 234 patients were reviewed, with a mean follow‐up of 22 months (3 months–64 months). Rhinologic, psychological, and sleep subdomains significantly improved versus baseline (all p < 0.05). Subanalysis of 40 patients with follow‐up at all timepoints showed statistically significant improvement in rhinologic, extra‐nasal, psychological, and sleep subdomains, with minimal clinically important difference met between 2 and 5 years in sleep and psychological subdomains. Adjuvant chemoradiation was associated with worse outcomes in rhinologic (adjusted odds ratio (5.22 [1.69–8.66])), extra‐nasal (2.21 [0.22–4.17]) and ear/facial (5.53 [2.10–8.91]) subdomains. Pterygopalatine fossa involvement was associated with worse outcomes in rhinologic (3.22 [0.54–5.93]) and ear/facial (2.97 [0.32–5.65]) subdomains. Positive margins (5.74 [2.17–9.29]) and surgical approach—combined versus endoscopic (3.41 [0.78–6.05])—were associated with worse psychological outcomes. Adjuvant radiation (2.28 [0.18–4.40]) was associated with worse sleep outcomes.ConclusionsSinonasal QOL improvements associated with treatment of SNM are driven by rhinologic, extra‐nasal, psychological, and sleep subdomains.

OBJECTIVE Chordomas are rare tumors that often recur regardless of surgery with negative margins and postoperative radiotherapy. The predictive accuracy of widely used immunohistochemical (IHC) markers in addressing the recurrence of skull base chordomas (SBCs) is yet to be determined. This study aimed to investigate IHC markers in the prediction of recurrence after SBC resection with adjuvant radiation therapy. METHODS The authors reviewed the records of patients who had treatment for SBC between January 2017 and June 2021 across the Mayo Clinic in Minnesota, Florida, and Arizona. Exclusion criteria included patients who had no histopathology or recurrence as an outcome. Histopathological markers included cytokeratin A1/A3 only, epithelial membrane antigen (EMA), S100 protein, pan-cytokeratin, IN1, GATA3, CAM5.2, OSCAR, and chondroid. Information from patient records was abstracted, including treatment, clinical and radiological follow-up duration, demographics, and histopathological factors. Decision tree and random forest classifiers were trained and tested to predict the recurrence based on unseen data using an 80/20 split. RESULTS A total of 38 patients with a diagnosis of SBC who underwent resection (gross-total resection: 42.1%; and subtotal resection: 57.9%) and radiation therapy were extracted from the medical records. The mean patient age was 48.2 (SD 19.6) years; most patients were male (n = 23; 60.5%) and White (n = 36; 94.7%). Pan-cytokeratin was associated with an increased risk of postoperative recurrence (OR 14.67, 95% CI 2.44–88.13; p = 0.00517) after resection and adjuvant radiotherapy. The decision tree analysis found pan-cytokeratin–positive tumors to have a 78% chance of being classified as a recurrence, with an accuracy of 75%. The distribution of minimal depth in the prediction of postoperative recurrence indicates that the most important variables were pan-cytokeratin, followed by cytokeratin A1/A3 and EMA. CONCLUSIONS The authors’ machine learning algorithm identified pan-cytokeratin as the largest contributor to recurrence among other IHC markers after SBC resection. Machine learning may facilitate the prediction of outcomes in rare tumors, such as chordomas.

Abstract Introduction The transbasal approach traditionally uses a bicoronal scalp incision with bifrontal craniotomy to establish an extradural midline skull base working corridor. Depending on additional craniofacial osteotomies, this approach can expand its reach to the nasal cavity and paranasal sinuses and may be employed for the resection of particularly complex sinonasal and midline skull base tumors. Given its discrepancy in nomenclature and differences in interoperator technique, we propose a practical, operatively oriented guide for trainees performing this approach. Methods Three formalin-fixed, latex-injected specimens were dissected under microscopic magnification and endoscopic-assisted visualization. Stepwise dissections of the transcranial-transbasal approach with common modifications were performed, documented with three-dimensional photography, and supplemented with representative case applications. Results The traditional transbasal approach via bifrontal craniotomy affords wide extradural access to the anterior cranial fossa and central skull base. The addition of craniofacial osteotomies further expands access into the sinonasal cavities, clivus, and craniocervical junction. Key steps described include patient positioning, bicoronal skin incision, pericranial graft harvest, bifrontal craniotomy, orbital rim osteotomy, sphenoidotomy, bilateral ethmoidectomies, and microsurgical dissection of the sellar region. Basal superior sagittal sinus ligation and durotomy allow for intradural exposure. Reconstruction techniques are also discussed. Conclusion While the transbasal approach is rich with historical descriptions, illustrations, and modifications, its stepwise performance may be relatively unknown and unclear to younger generations of trainees. We present a comprehensive guide to optimize familiarity with the transbasal approach and its indications in the surgical anatomy laboratory, mastery of the relevant microsurgical anatomy, and simultaneous preparation for learning and participation in the operating room.