Purpose Biochemical remission (BR), gross total resection (GTR), and intraoperative cerebrospinal fluid (CSF) leaks are important metrics in transsphenoidal surgery for acromegaly, and prediction of their likelihood using machine learning would be clinically advantageous. We aim to develop and externally validate clinical prediction models for outcomes after transsphenoidal surgery for acromegaly. Methods Using data from two registries, we develop and externally validate machine learning models for GTR, BR, and CSF leaks after endoscopic transsphenoidal surgery in acromegalic patients. For the model development a registry from Bologna, Italy was used. External validation was then performed using data from Zurich, Switzerland. Gender, age, prior surgery, as well as Hardy and Knosp classification were used as input features. Discrimination and calibration metrics were assessed. Results The derivation cohort consisted of 307 patients (43.3% male; mean [SD] age, 47.2 [12.7] years). GTR was achieved in 226 (73.6%) and BR in 245 (79.8%) patients. In the external validation cohort with 46 patients, 31 (75.6%) achieved GTR and 31 (77.5%) achieved BR. Area under the curve (AUC) at external validation was 0.75 (95% confidence interval: 0.59–0.88) for GTR, 0.63 (0.40–0.82) for BR, as well as 0.77 (0.62–0.91) for intraoperative CSF leaks. While prior surgery was the most important variable for prediction of GTR, age, and Hardy grading contributed most to the predictions of BR and CSF leaks, respectively. Conclusions Gross total resection, biochemical remission, and CSF leaks remain hard to predict, but machine learning offers potential in helping to tailor surgical therapy. We demonstrate the feasibility of developing and externally validating clinical prediction models for these outcomes after surgery for acromegaly and lay the groundwork for development of a multicenter model with more robust generalization.

Objective Pituitary neuroendocrine tumours (PitNET)s can be aggressive, thus presenting local invasion, postsurgical recurrence and/or resistance to treatment, responsible for significant morbidity. The study aimed at identifying prognostic factors of postsurgical outcome using data‐driven classification of patients. Design Retrospective observational study. Methods Clinicopathological and radiological data of patients with PitNET treated via endoscopic endonasal surgery were collected. Tumour recurrence/progression and progression‐free survival were assessed by classification tree analysis (CTA) and Kaplan‐Meier curves, respectively. Histological subtype, cavernous/sphenoid sinus invasion, mitosis, Ki‐67, p53, Trouillas’ grading, degree of tumour exeresis and postsurgery disease activity were also evaluated. Results A total of 1066 (466 gonadotroph, 287 somatotroph, 148 lactotroph, 157 corticotroph and 8 thyrotroph) tumours were included; 21.7% invaded the cavernous/sphenoid sinus. Based on Trouillas’ classification, 64.3% were grade 1a, 14.2% 1b, 16.1% 2a, and 5.4% 2b; 18.3% had >2/10 HPF mitoses, 24.9% had Ki‐67 ≥3%; 15.8% were positive for p53. Exeresis was radical in 81.2% of the cases. Median follow‐up was 59.2 months. At last evaluation, 79.4% of the patients were cured; 20.6% had disease persistence, controlled by medical treatment in 18.3% of them. Disease recurrence/progression was recorded in 10.9% of the cases. CTA identified 5 distinct patient subgroups with different risk of disease recurrence/progression. Grade 2 of the Trouillas’ grading, >2/10 HPF mitoses, Ki‐67 ≥3%, p53 protein expression (P < .001), tumour invasion (P = .002) and ACTH‐subtype (P = .003) were identified as risk factors of disease recurrence/progression. Conclusions The combined evaluation of Trouillas’ grading, proliferation indexes and immunohistochemistry appears promising in the prediction of surgical outcome in PitNET.

Multiple eruptive cutaneous non-melanoma skin cancers(NMSCs) have been reported to arise at the sites of skin surgery, including the area affected by the primary tumour and split thickness skin graft(STSG) donor and recipient sites. The aim of this study is to make a critical revision of the literature, analysing the clinical, histological features and outcomes of eruptive NMSCs after cutaneous surgery. Up to August 2021, according to our systematic review of the literature, we have collected 18 published articles and a total of 33 patients, including our two cases. The results of this review highlight five red flags that clinicians should consider: (i) lower and upper limbs represent the cutaneous site with the highest risk, representing 82.35% of the cases in the literature; (ii) the median time to onset of eruptive NMSCs that is approximately 6 weeks; (iii) primary NMSCs were completely excised with free margins on histologic examination in all cases of the literature, and therefore the eruptive NMSCs reported could not be considered recurrences; (iv)  any surgical technique involves a risk to promote eruptive NMSCs; (v) treatment of eruptive NMSCs includes surgery or combined surgical and medical treatment. However, eruptive NMSCs recurrences are a real medical challenge and have always been treated combining surgical and medical treatment, with complete resolution in about one third of patients.  In conclusion, even though the pathogenesis remains unclear, this review highlights 5 red flags which could support clinicians in the diagnosis and management of eruptive of NMSCs after skin surgery.