Object The authors report a series of eight consecutive cases in which epidural abscesses in the cervical spine were treated by microsurgery without arthrodesis, including two cases of concomitant pyogenic and tubercular infection. Methods The authors used a minimally invasive surgical approach consisting of single-level anterior microsurgical discectomy and drainage of the epidural abscess via a silicone catheter, and then initiated antibiotic therapy. At follow-up examination (mean duration 39 months), six patients exhibited complete recovery and two suffered from minor residual deficits. In all cases, spontaneous vertebral fusion occurred. Sagittal alignment was maintained in seven patients, and in one there was slight asymptomatic kyphosis. In two patients, tubercular and pyogenic infections were found. Prior intervention for dental infection was recorded in four cases. Conclusions In the absence of preoperative spinal instability, microsurgical drainage of the abscess followed by specific antibiotic therapy resulted in spinal cord decompression and neurological recovery, thereby facilitating spontaneous fusion and vertebral stability. The presence of combined tubercular and pyogenic infections of the cervical spine should be considered, especially in patients whose immune systems are depressed.

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In our series, 96% of patients had a sustained favourable long-term clinical outcome after ACD. These favourable results confirm data in the literature and support our preference for ACD as the simplest, fastest and cheapest surgical option for treating myeloradiculopathy secondary to one- to two-level cervical discoarthrosis.

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Papillary glioneuronal tumor (PGNT) is a recently described central nervous system neoplasm that mostly occurs in the supratentorial system, adjacent to the lateral ventricles. Well-established examples of glioneuronal tumors include DNET (Dysembryoplastic NeuroEpithelial Tumor), ganglioglioma and desmoplastic infantile ganglioglioma. More recently recognized entities include rosette-forming tumor of fourth ventricle, rosette glioneuronal tumor (RGNT)/ glioneuronal tumor with neuropil-like islands and PGNT (1).In 2007, WHO classified PGNT as grade I neuronal-glial tumor because of the characteristic papillary architecture and bipartite (astrocytic and neuronal/neurocytic) cell population. As a newly established entity of mixed glioneuronal tumor family, PGNT attracted extensive attention recently.It was described in approximately 67 cases to date (Table 1).Both CT and MRI studies show PGNTs to be circumscribed supratentorial lesions, extending to the cortex and the white matter, or may be more deeply situated, displacing the ventricular system. Cystic alteration are frequent. Solid components are iso/hypointense in T1 and iso/hyperintense in T2, contrast enhancing in a diffuse, patchy or rimming fashion and may present as mural nodule or plaques within otherwise cystic masses. Calcifications may be seen. Bleeding of lesions are rare but recorded (3), associated with superficial siderosis of neuraxis. Surrounding edema and mass effect are seen in larger lesions. Reported tumor size ranges 1-9 cm (mean 4,5 cm).Hystology showed pseudopapillary pattern consisting of a single layer of astrocytic cells surrounding hyalinized blood cells.Follow-up indicated that this type of tumor had a benign behavior even if recently we found out 5 cases of aggressive PGNT (1,2,4,5,7,8). Case reportThe patient was a 34 yr old woman with complaints of progressive headache started 2 weeks before and more recently confusion, vomiting and dizziness. She had a va-

Background Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH. Methods We consulted electronic records of patients with aneurysmal SAH treated at our institution between January 2013 and March 2019. We selected variables for the models according to the results of the previous works on this topic. We trained and tested four ML algorithms on three datasets: one containing binary variables, one considering variables associated with shunt-dependency after an explorative analysis, and one including all variables. For each model, we calculated AUROC, specificity, sensitivity, accuracy, PPV, and also, on the validation set, the NPV and the Matthews correlation coefficient (ϕ). Results Three hundred eighty-six patients were included. Fifty patients (12.9%) developed shunt-dependency after a mean follow-up of 19.7 (± 12.6) months. Complete information was retrieved for 32 variables, used to train the models. The best models were selected based on the performances on the validation set and were achieved with a distributed random forest model considering 21 variables, with a ϕ = 0.59, AUC = 0.88; sensitivity and specificity of 0.73 (C.I.: 0.39–0.94) and 0.92 (C.I.: 0.84–0.97), respectively; PPV = 0.59 (0.38–0.77); and NPV = 0.96 (0.90–0.98). Accuracy was 0.90 (0.82–0.95). Conclusions Machine learning prognostic models allow accurate predictions with a large number of variables and a more subject-oriented prognosis. We identified a single best distributed random forest model, with an excellent prognostic capacity (ϕ = 0.58), which could be especially helpful in identifying low-risk patients for shunt-dependency.

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