MedElevator Войти в исследования
← Поиск публикаций

DOI 10.1016/j.wneu.2019.01.163

Медицинская библиотека · World neurosurgery · 2019

Identification of Preoperative Language Tracts for Intrinsic Frontotemporal Diseases: A Pilot Reconstruction Algorithm in a Middle-Income Country.

Ordóñez-Rubiano Edgar G, Valderrama-Arias Francisco A, Forbes Jonathan A, Johnson Jason M, Younus Iyan, Marín-Muñoz Jorge H, Sánchez-Montaño Mariana, Angulo Diego A, Cifuentes-Lobelo Hernando A, Cortes-Lozano William, Pedraza-Ciro Maria C, Bello-Dávila May Leonardo, Patiño-Gómez Javier G, Ordóñez-Mora Edgar G

СтатьяПеревод пока не подготовлен

Аннотация

OBJECTIVE

Diffusion tensor imaging (DTI) tractography provides useful information that can be used to optimize surgical planning and help avoid injury during subcortical dissection of eloquent tracts. The objective is to provide a safe, timely, and affordable algorithm for preoperative DTI language reconstruction for intrinsic frontotemporal diseases.

METHODS

We reviewed a prospectively acquired database of preoperative DTI reconstruction for resection of left frontotemporal lesions over 3 years at Hospital de San José and Hospital Infantil Universitario San José, Fundación Universitaria de Ciencias de la Salud, Bogota, Colombia. Preoperative and postoperative clinical and radiographic features were determined from retrospective chart review. A comprehensive review of the structural and functional anatomy of the language tracts was performed. Separate reconstruction of both ventral (semantic) and dorsal (phonologic) stream pathways is described: arcuate fasciculus, superior longitudinal fasciculus, inferior fronto-occipital fasciculus, uncinate fasciculus, and inferior longitudinal fasciculus.

RESULTS

Between January 2015 and January 2018, 44 tumor cases were found to be resected with preoperative fiber tracking planning and neuronavigation-guided surgery. Ten patients (7 women, 3 men) aged 28-65 years underwent resection of an intrinsic frontotemporal lesion with preoperative DTI tractography reconstruction of language tracts. Eight cases (80%) were high-grade gliomas and 2 (20%) were cavernous malformations. In 5 cases (50%), the lesion was in the frontal lobe and in 5 (50%), it was in the temporal lobe. The extent of resection was classified as gross total resection (100%), subtotal resection (>90%), or partial resection (<90%). Gross total resection was achieved in 5 cases (50%), subtotal resection was achieved in 4 cases (40%), and partial resection in the remaining case (10%). Compromised tracts included superior longitudinal fasciculus in 7 (70%), inferior longitudinal fasciculus in 4 (40%), the arcuate fasciculus in 3 (30%), and uncinate fasciculus in 1 (10%). Language function was unchanged or improved in 90% of patients. New-onset postoperative language decline occurred in 1 patient, who recovered transient phonemic paraphasias 1 month after resection. The mean follow-up time was 7 months (range, 4-12 months). Residual tumors were treated with radiation and/or with chemotherapy as indicated in an outpatient setting.

CONCLUSIONS

We present a safe and efficacious preoperative DTI language reconstruction algorithm that could be used as a feasible treatment strategy in a challenging subset of tumors in low- to middle-income countries.

Для цитирования: Ordóñez-Rubiano Edgar G, Valderrama-Arias Francisco A, Forbes Jonathan A, Johnson Jason M, Younus Iyan, Marín-Muñoz Jorge H, Sánchez-Montaño Mariana, Angulo Diego A, Cifuentes-Lobelo Hernando A, Cortes-Lozano William, Pedraza-Ciro Maria C, Bello-Dávila May Leonardo, Patiño-Gómez Javier G, Ordóñez-Mora Edgar G. Identification of Preoperative Language Tracts for Intrinsic Frontotemporal Diseases: A Pilot Reconstruction Algorithm in a Middle-Income Country.. World neurosurgery. 2019. DOI: 10.1016/j.wneu.2019.01.163.

Предметные рубрики MeSH

Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.