{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T02:01:01Z","timestamp":1790388061282,"version":"4.1.0"},"reference-count":37,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>The aim of this study was to develop and validate an anatomy-driven model that predicts gross-total resection (GTR) of skull base chondrosarcomas (SBCs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>The authors conducted a retrospective multi-institutional analysis of consecutive SBCs resected at 4 academic centers in 2000\u20132024. Thirteen preoperative variables of interest included tumor location (petroclival, peri-lacerum, anterior, or lateral skull base), maximal tumor diameter, internal carotid artery (ICA) encasement, compartmental extension, cranial nerve (CN) involvement, prior radiotherapy and\/or surgery, synchondrosis of origin, and planned operative approach. Data were split into 75% training (n = 139) and 25% validation (n = 46) sets. Five algorithms were tuned: generalized linear model (GLM), random forest, support vector machine, k-nearest neighbors, and eXtreme Gradient Boosting. For interpretability, a multivariable logistic regression model was fitted and a nomogram for predicting GTR was built. Discrimination performance was summarized by the area under the curve (AUC), accuracy, sensitivity, specificity, and Brier score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>A total of 185 consecutive SBCs were identified. Anatomical burden varied by location: cavernous sinus invasion clustered in peri-lacerum (64.5%) and petroclival (57.1%) SBCs, jugular foramen extension occurred largely in petroclival cases (38.1%), and sinonasal or orbital spread characterized anterior midline lesions. Surgical approaches mirrored this distribution, endonasal routes for peri-lacerum (80%) and petroclival (63.4%) tumors, whereas open approaches predominated for lateral tumors (84.6%). The overall GTR rate was 56%, with rates of 52.4%, 67.7%, 76.9%, and 66.7% for petroclival, peri-lacerum, lateral, and midline tumors, respectively (p = 0.186). For petroclival disease, the surgical choice was decisive (p &lt; 0.001), with GTR rates of 72.1% for the endoscopic transpterygoid approach (ETPA), 25.7% for open surgery, and 41.7% for the midline endoscopic endonasal approach (EEA). Multivariable analysis demonstrated significantly decreased odds of GTR with graded ICA encasement (ORs 3.12 for 90\u00b0\u2013180\u00b0, 7.41 for 181\u00b0\u2013270\u00b0, 9.76 for 271\u00b0\u2013359\u00b0, 8.52 for 360\u00b0, compared with no encasement), prior radiotherapy (OR 4.04), petroclival location (OR 4.72), lateral location (OR 3.29), infratemporal fossa extension (OR 2.75), and CN VII\u2013VIII involvement (OR 2.82). The ETPA, as compared to open surgery, was independently associated with higher odds of GTR (i.e., decreased odds of subtotal resection; OR 0.22, p &lt; 0.001), with a favorable trend for midline EEA (OR 0.37, p = 0.063). The GLM exhibited the best discrimination with an AUC of 0.838 and Brier score of 0.19 (accuracy 0.898).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>Anatomical factors, especially petroclival tumor origin, ICA encasement, and lower CN corridors, are the principal barriers to complete resection of SBCs. The machine leaning\u2013supported nomogram provides a reproducible preoperative tool aligning corridor choice with anatomy, improving GTR likelihood, and rationalizing adjuvant therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2026.2.jns252854","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:01:39Z","timestamp":1784678499000},"page":"1-12","source":"Crossref","is-referenced-by-count":0,"title":["Predicting gross-total resection in skull base chondrosarcoma: a multi-institutional machine learning study"],"prefix":"10.3171","author":[{"given":"Juan Pablo","family":"Zuluaga-Garcia","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franco","family":"Rubino","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Baptist Medical Center, Jacksonville, Florida;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esteban","family":"Ramirez Ferrer","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios A.","family":"Zenonos","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Algattas","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, Buffalo;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Gardner","sequence":"additional","affiliation":[{"name":"Deparment of Neurosurgery, New York Grossman School of Medicine, New York, New York;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Carlos","family":"Fernandez-Miranda","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, California; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vera","family":"Vigo","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, California; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel M.","family":"Prevedello","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, The Ohio State University, Columbus, Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franco","family":"DeMonte","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaan M.","family":"Raza","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas;"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"issue":"6","key":"b1","series-title":"Cancers (Basel)","first-page":"1703","article-title":"Classification of chondrosarcoma: from characteristic to challenging imaging findings","volume":"15","author":"Kim JH","year":"2023","unstructured":"Kim JH, Lee SK. 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