TY - JOUR
T1 - Immunohistochemical markers predicting long-term recurrence following clival and spinal chordoma resection
T2 - a multicenter study
AU - Ghaith, Abdul Karim
AU - Akinduro, Oluwaseun O.
AU - Alexander, A. Yohan
AU - Goyal, Anshit
AU - Bon-Nieves, Antonio
AU - de Macedo Filho, Leonardo
AU - Otamendi-Lopez, Andrea
AU - Nathani, Karim Rizwan
AU - Abode-Iyamah, Kingsley
AU - Jentoft, Mark E.
AU - Bendok, Bernard R.
AU - Clarke, Michelle J.
AU - Link, Michael J.
AU - Gompel, Jamie J.Van
AU - Quiñones-Hinojosa, Alfredo
AU - Bydon, Mohamad
N1 - Publisher Copyright:
© AANS 2023, except where prohibited by US copyright law
PY - 2023
Y1 - 2023
N2 - OBJECTIVE Chordomas are rare tumors from notochordal remnants and account for 1%–4% of all primary bone malignancies, often arising from the clivus and sacrum. Despite margin-negative resection and postoperative radiotherapy, chordomas often recur. Further, immunohistochemical (IHC) markers have not been assessed as predictive of chordoma recurrence. The authors aimed to identify the IHC markers that are predictive of postoperative long-term (≥ 1 year) chordoma recurrence by using trained multiple tree-based machine learning (ML) algorithms. METHODS The authors reviewed the records of patients who had undergone treatment for clival and spinal chordomas between January 2017 and June 2021 across the Mayo Clinic enterprise (Minnesota, Florida, and Arizona). Demographics, type of treatment, histopathology, and other relevant clinical factors were abstracted from each patient record. Decision tree and random forest classifiers were trained and tested to predict long-term recurrence based on unseen data using an 80/20 split. RESULTS One hundred fifty-one patients diagnosed and treated for chordomas were identified: 58 chordomas of the clivus, 48 chordomas of the mobile spine, and 45 chordomas sacrococcygeal in origin. Patients diagnosed with cervical chordomas were the oldest among all groups (58 ± 14 years, p = 0.009). Most patients were male (n = 91, 60.3%) and White (n = 139, 92.1%). Most patients underwent resection with or without radiation therapy (n = 129, 85.4%). Subtotal resection followed by radiation therapy (n = 51, 33.8%) was the most common treatment modality, followed by gross-total resection then radiation therapy (n = 43, 28.5%). Multivariate analysis showed that S100 and pan-cytokeratin are more likely to predict the increase in the risk of postoperative recurrence (OR 3.67, 95% CI 1.09–12.42, p = 0.03; and OR 3.74, 95% CI 0.05–2.21, p = 0.02, respectively). In the decision tree analysis, a clinical follow-up > 1897 days was found in 37% of encounters and a 90% chance of being classified for recurrence (accuracy = 77%). Random forest analysis (n = 500 trees) showed that patient age, type of surgical treatment, location of tumor, S100, pan-cytokeratin, and EMA are the factors predicting long-term recurrence. CONCLUSIONS The IHC and clinicopathological variables combined with tree-based ML tools successfully demonstrated a high capacity to identify recurrence patterns with an accuracy of 77%. S100, pan-cytokeratin, and EMA were the IHC drivers of recurrence. This shows the power of ML algorithms in analyzing and predicting outcomes of rare conditions of a small sample size.
AB - OBJECTIVE Chordomas are rare tumors from notochordal remnants and account for 1%–4% of all primary bone malignancies, often arising from the clivus and sacrum. Despite margin-negative resection and postoperative radiotherapy, chordomas often recur. Further, immunohistochemical (IHC) markers have not been assessed as predictive of chordoma recurrence. The authors aimed to identify the IHC markers that are predictive of postoperative long-term (≥ 1 year) chordoma recurrence by using trained multiple tree-based machine learning (ML) algorithms. METHODS The authors reviewed the records of patients who had undergone treatment for clival and spinal chordomas between January 2017 and June 2021 across the Mayo Clinic enterprise (Minnesota, Florida, and Arizona). Demographics, type of treatment, histopathology, and other relevant clinical factors were abstracted from each patient record. Decision tree and random forest classifiers were trained and tested to predict long-term recurrence based on unseen data using an 80/20 split. RESULTS One hundred fifty-one patients diagnosed and treated for chordomas were identified: 58 chordomas of the clivus, 48 chordomas of the mobile spine, and 45 chordomas sacrococcygeal in origin. Patients diagnosed with cervical chordomas were the oldest among all groups (58 ± 14 years, p = 0.009). Most patients were male (n = 91, 60.3%) and White (n = 139, 92.1%). Most patients underwent resection with or without radiation therapy (n = 129, 85.4%). Subtotal resection followed by radiation therapy (n = 51, 33.8%) was the most common treatment modality, followed by gross-total resection then radiation therapy (n = 43, 28.5%). Multivariate analysis showed that S100 and pan-cytokeratin are more likely to predict the increase in the risk of postoperative recurrence (OR 3.67, 95% CI 1.09–12.42, p = 0.03; and OR 3.74, 95% CI 0.05–2.21, p = 0.02, respectively). In the decision tree analysis, a clinical follow-up > 1897 days was found in 37% of encounters and a 90% chance of being classified for recurrence (accuracy = 77%). Random forest analysis (n = 500 trees) showed that patient age, type of surgical treatment, location of tumor, S100, pan-cytokeratin, and EMA are the factors predicting long-term recurrence. CONCLUSIONS The IHC and clinicopathological variables combined with tree-based ML tools successfully demonstrated a high capacity to identify recurrence patterns with an accuracy of 77%. S100, pan-cytokeratin, and EMA were the IHC drivers of recurrence. This shows the power of ML algorithms in analyzing and predicting outcomes of rare conditions of a small sample size.
KW - chordoma
KW - clivus
KW - decision tree
KW - machine learning
KW - random forest
KW - spine
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UR - http://www.scopus.com/inward/citedby.url?scp=85161275889&partnerID=8YFLogxK
U2 - 10.3171/2023.3.FOCUS22653
DO - 10.3171/2023.3.FOCUS22653
M3 - Article
C2 - 37552641
AN - SCOPUS:85161275889
SN - 1092-0684
VL - 54
JO - Neurosurgical focus
JF - Neurosurgical focus
IS - 6
M1 - E15
ER -