Surgical outcome and indicators of postoperative worsening in intra-axial thalamic and posterior fossa pediatric tumors: Preliminary results from a single tertiary referral center cohort

Background: Shared indications about the best management of intra-axial thalamic (IAT) and posterior fossa (PF) pediatric tumors are still lacking. The aim of this study was to analyze neurosurgical outcome in these tumors and to investigate factors associated with postoperative worsening. Methods: A retrospective single-center study on IAT and PF pediatric tumor patients treated surgically over a 7-year period was conducted. The Lansky Scale (LS) was used to assess patients' functional status. Surgical complexity was graded with the Milan Complexity Scale (MCS). The following analyses were performed: a longitudinal analysis of the preoperative, discharge, and 3 months' follow-up (FU) LS, a comparison between improved/unchanged and worsened patients, and an analysis of the predictive value of single MCS items. Results: 37 cases were collected: 20 PF and 17 thalamic. Mean MCS score was 6 ± 1.7. Mean preoperative, discharge and FU LS were 80.8, 74.6 and 80.3 respectively. Surgical mortality was 0%.The longitudinal analysis showed a neurological worsening at discharge compared to preoperative status (p = 0.011) and an improvement at FU compared to discharge (p < 0.004), both statistically significant. None of the variables analyzed showed a significant predictive value of early postoperative change; however, higher MCS scores were associated with a greater risk of worsening. Conclusions: The surgical management of IAT and PF pediatric brain tumors remains challenging; early postoperative worsening is possible, but most deficits tend to improve at FU. The MCS seems to be a valuable tool to estimate the risk of early postoperative worsening and to facilitate parents' informed consent

Neural network–based identification of patients at high risk for intraoperative cerebrospinal fluid leaks in endoscopic pituitary surgery

OBJECTIVE: Although rates of postoperative morbidity and mortality have become relatively low in patients undergoing transnasal transsphenoidal surgery (TSS) for pituitary adenoma, cerebrospinal fluid (CSF) fistulas remain a major driver of postoperative morbidity. Persistent CSF fistulas harbor the potential for headache and meningitis. The aim of this study was to investigate whether neural network–based models can reliably identify patients at high risk for intraoperative CSF leakage.METHODSFrom a prospective registry, patients who underwent endoscopic TSS for pituitary adenoma were identified. Risk factors for intraoperative CSF leaks were identified using conventional statistical methods. Subsequently, the authors built a prediction model for intraoperative CSF leaks based on deep learning.RESULTSIntraoperative CSF leaks occurred in 45 (29%) of 154 patients. No risk factors for CSF leaks were identified using conventional statistical methods. The deep neural network–based prediction model classified 88% of patients in the test set correctly, with an area under the curve of 0.84. Sensitivity (83%) and specificity (89%) were high. The positive predictive value was 71%, negative predictive value was 94%, and F1 score was 0.77. High suprasellar Hardy grade, prior surgery, and older age contributed most to the predictions.CONCLUSIONSThe authors trained and internally validated a robust deep neural network–based prediction model that identifies patients at high risk for intraoperative CSF. Machine learning algorithms may predict outcomes and adverse events that were previously nearly unpredictable, thus enabling safer and improved patient care and better patient counseling.</jats:sec

The impact of fluorescein-guided technique in the surgical removal of CNS tumors in a pediatric population: results from a multicentric observational study

BACKGROUND: Surgery has a fundamental role in central nervous system (CNS) tumors in the pediatric population, as aggressive resection correlates with prognosis. Due to its accumulation in areas with damaged blood brain barrier, sodium fluorescein (SF) could be a valid tool to improve the extent of resection in tumors enhancing at preoperative MRI. This study is aimed to systematically assess the utility of SF in a pediatric population. METHODS: Patient data were collected in two centers, one in Italy and the other in Germany. At the induction of anesthesia, SF was administered intravenously (5 mg/kg). Surgery was performed using a YELLOW560 filter. Fluorescence intensity was graduated as bright, moderate or absent based on surgeon's opinion; furthermore, SF use was judged as "helpful." "not helpful" or "not essential" in tumor removal. RESULTS: Twenty-four patients for 27 surgical procedures were identified. In 21 of 27 (77.8%) procedures fluorescence was reported as bright or moderate, in two of 27 (7.4%) absent and in four of 27 (14.8%) data were unavailable. Intraoperative fluorescence was reported in 21 of 25 (84%) surgeries whose corresponding preoperative MRI had shown contrast enhancement. In 14 of 27 (51.8%) surgical procedures SF was considered "helpful"; in two of 27 (7.4%) not "helpful"; in seven of 27 (25.9%) "not essential." In four of 27 (14.8%) data were unavailable. No adverse effect to SF was registered. CONCLUSIONS: SF could be considered a valid and safe tool to improve visualization of tumors enhancing at preoperative MRI also in pediatric patients. Future prospective studies are needed to confirm these preliminary' data

Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery

OBJECTIVE Decision-making for intracranial tumor surgery requires balancing the oncological benefit against the risk for resection-related impairment. Risk estimates are commonly based on subjective experience and generalized numbers from the literature, but even experienced surgeons overestimate functional outcome after surgery. Today, there is no reliable and objective way to preoperatively predict an individual patient's risk of experiencing any functional impairment. METHODS The authors developed a prediction model for functional impairment at 3 to 6 months after microsurgical resection, defined as a decrease in Karnofsky Performance Status of ≥ 10 points. Two prospective registries in Switzerland and Italy were used for development. External validation was performed in 7 cohorts from Sweden, Norway, Germany, Austria, and the Netherlands. Age, sex, prior surgery, tumor histology and maximum diameter, expected major brain vessel or cranial nerve manipulation, resection in eloquent areas and the posterior fossa, and surgical approach were recorded. Discrimination and calibration metrics were evaluated. RESULTS In the development (2437 patients, 48.2% male; mean age ± SD: 55 ± 15 years) and external validation (2427 patients, 42.4% male; mean age ± SD: 58 ± 13 years) cohorts, functional impairment rates were 21.5% and 28.5%, respectively. In the development cohort, area under the curve (AUC) values of 0.72 (95% CI 0.69-0.74) were observed. In the pooled external validation cohort, the AUC was 0.72 (95% CI 0.69-0.74), confirming generalizability. Calibration plots indicated fair calibration in both cohorts. The tool has been incorporated into a web-based application available at https://neurosurgery.shinyapps.io/impairment/. CONCLUSIONS Functional impairment after intracranial tumor surgery remains extraordinarily difficult to predict, although machine learning can help quantify risk. This externally validated prediction tool can serve as the basis for case-by-case discussions and risk-to-benefit estimation of surgical treatment in the individual patient