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OBJECTIVE The goal of this study was to assess the social determinants that influence access and outcomes for pediatric neurosurgical care for patients with Chiari malformation type I (CM-I) and syringomyelia (SM). METHODS The authors used retro- and prospective components of the Park-Reeves Syringomyelia Research Consortium database to identify pediatric patients with CM-I and SM who received surgical treatment and had at least 1 year of follow-up data. Race, ethnicity, and insurance status were used as comparators for preoperative, treatment, and postoperative characteristics and outcomes. RESULTS A total of 637 patients met inclusion criteria, and race or ethnicity data were available for 603 (94.7%) patients. A total of 463 (76.8%) were non-Hispanic White (NHW) and 140 (23.2%) were non-White. The non-White patients were older at diagnosis (p = 0.002) and were more likely to have an individualized education plan (p < 0.01). More non-White than NHW patients presented with cerebellar and cranial nerve deficits (i.e., gait ataxia [p = 0.028], nystagmus [p = 0.002], dysconjugate gaze [p = 0.03], hearing loss [p = 0.003], gait instability [p = 0.003], tremor [p = 0.021], or dysmetria [p < 0.001]). Non-White patients had higher rates of skull malformation (p = 0.004), platybasia (p = 0.002), and basilar invagination (p = 0.036). Non-White patients were more likely to be treated at low-volume centers than at high-volume centers (38.7% vs 15.2%; p < 0.01). Non-White patients were older at the time of surgery (p = 0.001) and had longer operative times (p < 0.001), higher estimated blood loss (p < 0.001), and a longer hospital stay (p = 0.04). There were no major group differences in terms of treatments performed or complications. The majority of subjects used private insurance (440, 71.5%), whereas 175 (28.5%) were using Medicaid or self-pay. Private insurance was used in 42.2% of non-White patients compared to 79.8% of NHW patients (p < 0.01). There were no major differences in presentation, treatment, or outcome between insurance groups. In multivariate modeling, non-White patients were more likely to present at an older age after controlling for sex and insurance status (p < 0.01). Non-White and male patients had a longer duration of symptoms before reaching diagnosis (p = 0.033 and 0.004, respectively). CONCLUSIONS Socioeconomic and demographic factors appear to influence the presentation and management of patients with CM-I and SM. Race is associated with age and timing of diagnosis as well as operating room time, estimated blood loss, and length of hospital stay. This exploration of socioeconomic and demographic barriers to care will be useful in understanding how to improve access to pediatric neurosurgical care for patients with CM-I and SM.

Abstract Introduction: Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology. However, these biological subgroups overlap clinically. While R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard of care for treating patients with DLBCL, predicting which patients will not benefit from such therapy is important so that alternative therapy or clinical trials can be considered. Most of the studies stratifying patients select biomarkers first, then explore how these biomarkers can stratify patients based on outcome. We explored the potential of using machine learning to first group patients with DLBCL based on survival, then isolating the biomarkers necessary for predicting these survival subgroups. Methods: RNA was extracted from tissue paraffin blocks from 379 R-CHOP treated patients with de novo DLBCL, and from 247 patients with extranodal DLBCL. A targeted hybrid capture RNA panel of 1408 genes was used for next generation sequencing (NGS). Sequencing was performed using an Illumina NextSeq 550 System platform. Ten million reads per sample in a single run were required, and the read length was 2 × 150 bp. An expression profile was generated from the sequencing coverage profile of each individual sample using Cufflinks. A machine learning system was developed to classify patients into four groups based on their overall survival. This machine learning approach based on Naïve Bayesian algorithm was also used to discover the relevant subset of genes with which to classify patients into each of the four survival groups. To eliminate the underflow problem commonly associated with the standard Naïve Bayesian classifiers, we applied Geometric Mean Naïve Bayesian (GMNB) as the classifier to predict the survival group for each patient. Results: Using machine learning, patients were first divided into two groups: short survival (S) and long survival (L). To refine this model, we used the same approach and divided the patients in each group into two subgroups, generating four groups: long survival in the long group (LL), short survival in the long group (LS), long survival in the short group (SL), and short survival in the short group (SS). The hazard ratio for this model was 0.174 (confidence interval: 0.120-0.251), and P-value &lt;0.0001. After defining these four groups, a machine learning algorithm was used to discover the biomarkers from the expression data of the 1408 genes from NGS data. To reduce the effects of noise and avoid overfitting, we employed a 12-step cross validation to obtain a robust measure. For an individual gene, a generalized Naïve Bayesian classifier was constructed on the training of one of the 12 subsets and tested on the other 11 testing subsets. This allowed us to limit the prediction process to 60 genes for each separation step. Using the selected biomarkers, we classified the patients in the original set (379 patients) into LL, LS, SL, and SS groups and then evaluated the survival pattern of these groups. As shown in Fig. 1A, the selected biomarkers predicted survival as expected in the overall survival groups prior to biomarker selection. For additional validation of the system, we used the selected biomarkers to classify a completely new set of 247 samples of patients with extranodal DLBCL. As shown in Fig. 1B, these selected biomarkers successfully predicted the overall survival in this group of patients with an HR of 0.530 (confidence interval: 0.234-1.197, P=0.005). This classification correlated with cell of origin classification, TP53 mutation status, MYC expression, and IRF4 expression. However, in a multivariate analysis, only TP53 mutation was independent in predicting prognosis (P=0.005) and age (below or over 60) (P=0.01) along with the survival grouping (P&lt;0.000001). Conclusions: Using a novel machine learning approach with the expression levels of 180 genes, we developed a model that can reliably stratify patients with DLBCL treated with R-CHOP into four survival subgroups. This model can be used to identify patients who may not respond well to R-CHOP to be considered for alternative therapy and clinical trials. Figure 1 Figure 1. Disclosures Hsi: AbbVie Inc, Eli Lilly: Research Funding. Ferreri: Ospedale San Raffaele srl: Patents & Royalties; BMS: Research Funding; Pfizer: Research Funding; Beigene: Research Funding; Hutchison Medipharma: Research Funding; Amgen: Research Funding; Genmab: Research Funding; ADC Therapeutics: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; PletixaPharm: Membership on an entity's Board of Directors or advisory committees; x Incyte: Membership on an entity's Board of Directors or advisory committees; Adienne: Membership on an entity's Board of Directors or advisory committees. Piris: Millenium/Takeda, EUSA, Jansen, NanoString, Kyowa Kirin, Gilead and Celgene.: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Winter: BMS: Other: Husband: Data and Safety Monitoring Board; Actinium Pharma: Consultancy; Janssen: Other: Husband: Consultancy; Agios: Other: Husband: Consultancy; Gilead: Other: Husband: Consultancy; Epizyme: Other: Husband: Data and Safety Monitoring Board; Ariad/Takeda: Other: Husband: Data and Safety Monitoring Board; Merck: Consultancy, Honoraria, Research Funding; Novartis: Other: Husband: Consultancy, Data and Safety Monitoring Board; Karyopharm (Curio Science): Honoraria.

Abstract Background: CD20 antibody plus alkylator and/or anthracycline based immunochemotherapy (IC) is a standard frontline therapy for patients with follicular lymphoma (FL) with 10-year event-free survival (EFS) and overall survival (OS) rates of approximately 50% and 80% respectively in long-term follow-up of clinical trials. Currently available clinical prognostic indices for FL have been designed using PFS and OS endpoints. Early events, commonly defined as progression of disease within 24 months (POD24) or early transformation to a more aggressive histology, are associated with inferior outcomes and increased risk of death due to refractory FL. Timely identification of the minority of patients with elevated mortality risk might enhance clinical management and research strategies. The FLIPI24 Consortium was created to develop a clinical prognostic index using early events as the primary endpoint. We report the outcomes for the pooled cohort and investigate the implications of therapy patterns on potential model development. Methods: Individual patient data were pooled and harmonized from 11 prospective observational cohorts from Europe, North America, and Australia. Patients who were diagnosed with grades 1-3A FL and initiated frontline IC were eligible. EFS was defined as time from start of IC to progression, relapse, retreatment (2nd line), histologic transformation, or death due to any cause. Early events were defined using status at 24 months from start of IC. OS was defined as time from start of IC to death due to any cause. Kaplan Meier curves and Cox proportional hazards models were used to evaluate outcomes by clinical features and therapy types. Results: 9006 patients were abstracted and harmonized, 6111 patients initiated frontline IC between 2002 and 2018 and were included in this analysis. Median age at diagnosis was 61 years (IQR 52-69) and 50% were male. Complete FLIPI data were available in 5637 patients (92%) and 46%, 32%, and 22% were low, intermediate, and high risk, respectively. IC type was 3079 R-CHOP or like (50%) , 1529 R-CVP or like (25%), 918 R-bendamustine (B-R) or like (15%), and 585 fludarabine or other alkylator based IC (10%); 3187 received CD20 antibody maintenance (52%). Patients receiving R-CHOP were younger, more frequently grade 3A, and more frequently had elevated LDH; differences in other characteristics by IC type were not clinically meaningful. At median follow-up of 42 months (IQR 17-72), 2647 patients (43%) had an event (any) and 1494 patients (25%) died. Median survival after an early (non-death) event was 49 months (95% CI: 41-58); 5-year OS was 46% (95% CI: 43-49) compared to 89% (95% CI: 88-90) in patients without POD24. Across all IC types, EFS estimates at 2 and 10 years from start of IC were 80% (95% CI:79-81) and 49% (95% CI:48-51) and OS estimates were 92% (95% CI: 91-92) and 70% (95% CI: 69-72), respectively. FLIPI was highly associated with both EFS (c-statistic=0.61) and OS (c-statistic=0.65) from the initiation of IC (both p&lt;0.0001). There were significant differences in EFS and OS by IC type (both p&lt;0.0001) and use of maintenance was associated with prolonged EFS in landmark analyses at both 6 and 12 months from initiation of IC (both p&lt;0.0001). Treatment patterns changed significantly over the study timeframe. Use of B-R and/or maintenance increased to 30% and 70% respectively in N=2937 patients treated in 2010-2018 (Era2) compared to &lt;1% and 40% respectively in N=3174 patients treated 2002-2009 (Era1). EFS was significantly higher for Era2 compared to Era1 (HR=0.77, 95% CI: 0.71-0.83), which remained significant after adjustment for FLIPI (EFS HR=0.82, 95% CI: 0.76-0.89). However, the association between treatment eras and overall survival was weaker (HR=0.89, 95% CI: 0.79-0.99) and not significant after adjusting for baseline FLIPI (OS HR=0.99, 95% CI: 0.88-1.10). Conclusion: EFS and OS from this large pooled analysis of observational cohorts is similar to long-term follow-up of randomized clinical trials in the IC era and support the use of these data for model development. Modeling efforts for early events should adjust for initial IC selection and use of maintenance therapy. Utilization of bendamustine and/or maintenance therapy increased over the study timeframe from 2002-2018, and Era2 was associated with improved EFS but not OS. This cohort provides comprehensive and robust observational data to define clinical predictors in IC treated patients. Figure 1 Figure 1. Disclosures Maurer: Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Flowers: Janssen: Research Funding; Takeda: Research Funding; National Cancer Institute: Research Funding; Biopharma: Consultancy; BeiGene: Consultancy; Amgen: Research Funding; Celgene: Consultancy, Research Funding; Xencor: Research Funding; Acerta: Research Funding; Bayer: Consultancy, Research Funding; Sanofi: Research Funding; 4D: Research Funding; Adaptimmune: Research Funding; Allogene: Research Funding; EMD: Research Funding; TG Therapeutics: Research Funding; Burroughs Wellcome Fund: Research Funding; Kite: Research Funding; AbbVie: Consultancy, Research Funding; Cellectis: Research Funding; Denovo: Consultancy; Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research: Research Funding; Karyopharm: Consultancy; Gilead: Consultancy, Research Funding; Genmab: Consultancy; Epizyme, Inc.: Consultancy; Novartis: Research Funding; Nektar: Research Funding; Morphosys: Research Funding; Iovance: Research Funding; Spectrum: Consultancy; Pfizer: Research Funding; Ziopharm: Research Funding; Guardant: Research Funding; Eastern Cooperative Oncology Group: Research Funding; SeaGen: Consultancy; Pharmacyclics/Janssen: Consultancy; Genentech/Roche: Consultancy, Research Funding; Pharmacyclics: Research Funding. Villa: Janssen: Honoraria; Gilead: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Seattle Genetics: Honoraria; Celgene: Honoraria; Lundbeck: Honoraria; Roche: Honoraria; NanoString Technologies: Honoraria. Weibull: Jansen-Cilag: Other: part of a research collaboration between Karolinska Institutet and Janssen Pharmaceutica NV for which Karolinska Institutet has received grant support. Ghesquieres: Janssen: Honoraria; Mundipharma: Consultancy, Honoraria; Roche: Consultancy; Celgene: Consultancy, Honoraria; Gilead Science: Consultancy, Honoraria. Kridel: Gilead Sciences: Research Funding. Gandhi: Janssen: Research Funding; Novartis: Honoraria. Cheah: Celgene: Research Funding; TG Therapeutics: Consultancy, Honoraria, Other: advisory; Loxo/Lilly: Consultancy, Honoraria, Other: advisory; AstraZeneca: Consultancy, Honoraria, Other: advisory; AbbVie: Research Funding; Beigene: Consultancy, Honoraria, Other: advisory; Ascentage pharma: Consultancy, Honoraria, Other: advisory; Gilead: Consultancy, Honoraria, Other: advisory; MSD: Consultancy, Honoraria, Other: advisory, Research Funding; Janssen: Consultancy, Honoraria, Other: advisory; Roche: Consultancy, Honoraria, Other: advisory and travel expenses, Research Funding. Hawkes: Gilead: Membership on an entity's Board of Directors or advisory committees; Merck Sharpe Dohme: Membership on an entity's Board of Directors or advisory committees; Antigene: Membership on an entity's Board of Directors or advisory committees; Regeneron: Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squib/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck KgA: Research Funding; Specialised Therapeutics: Consultancy; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel and accommodation expenses, Research Funding, Speakers Bureau; Janssen: Speakers Bureau. Seymour: Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sunesis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Mei Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Morphosys: Honoraria, Membership on an entity's Board of Directors or advisory committees; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Research Funding, Speakers Bureau. Freeman: Amgen: Honoraria; Celgene: Honoraria; Sanofi: Honoraria, Speakers Bureau; Incyte: Honoraria; Abbvie: Honoraria; Teva: Research Funding; Roche: Research Funding; Janssen: Honoraria, Speakers Bureau; Seattle Genetics: Honoraria; Bristol Myers Squibb: Honoraria, Speakers Bureau. Clausen: Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expences ASH 2019; Gilead: Consultancy, Other: Travel expences 15th ICML ; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Wahlin: Gilead Sciences: Research Funding; Roche: Consultancy, Research Funding. Link: Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding; MEI: Consultancy. Ekstroem Smedby: Janssen Cilag: Research Funding; Takeda: Consultancy. Sehn: Genmab: Consultancy; Novartis: Consultancy; Debiopharm: Consultancy. Trněný: Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Portola: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; 1st Faculty of Medicine, Charles University, General Hospital in Prague: Current Employment; Celgene: Consultancy; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Amgen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria; Gilead Sciences: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; Incyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. El-Galaly: ROCHE Ltd: Ended employment in the past 24 months; Abbvie: Other: Speakers fee. Cerhan: Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding; NanoString: Research Funding.

Abstract BACKGROUND Diffuse leptomeningeal glioneuronal tumor (DLGNT), also known as oligodendrogliomatosis, is a rare neuro-oncologic condition along the neuraxis that remains poorly understood in children. We sought to describe our institutional experience and quantitively summarize the clinical survival and prognostic features of DLGNT in the pediatric population across the contemporary literature. METHODS We report four institutional cases of pediatric DLGNT diagnosed between 2000-2020 based on retrospective review of our records, and performed a comprehensive literature search for published cases from 2000 onwards to create an integrated cohort for analysis. Kaplan-Meier estimations, Fisher’s exact test, and logistic regression were utilized to interrogate the data. RESULTS Our overall integrated cohort consisted of 54 pediatric DLGNT patients, with 19 (35%) female and 35 (65%) male patients diagnosed at an average age of 6.4 years (range, 1.3-17 years) by means of surgical biopsy. Chemotherapy was used in 45 cases (83%), and mean follow-up time of 54 months (range, 3-204). Across the entire cohort, overall survival 1 month after diagnosis was 96% (95% CI 86-99%), and by 10 years was 69% (95% CI 49-82%). On multivariate analysis of complete data, chemotherapy treatment (HR=0.23, P=0.04) was statistically predictive of longer overall survival. When including limited data, longer duration of symptoms by presentation (HR=1.03, P=0.03) was statistically predictive of shorter overall survival. CONCLUSIONS This is the first quantitative study of pediatric DLGNT clinical course. More than 2-out-of-3 pediatric DLGNT patients survive beyond one decade. Chemotherapy is statistically associated with longer survival in DLGNT pediatric patients and should form the core of any treatment regimen in this setting. Early detection by means of judicious imaging and surgical biopsy for tissue diagnosis can lead to earlier treatment and likely superior outcomes.

BACKGROUND Mohs micrographic surgery (MMS) for cutaneous melanoma is becoming more prevalent, but surgical technique varies. OBJECTIVE To define variations in published techniques for MMS for melanoma. METHODS AND MATERIALS A systematic review was performed of PubMed, EMBASE, and Scopus databases to identify all articles describing surgical techniques for MMS for melanoma. Technical details were recorded for the preoperative, intraoperative, and postoperative phases of MMS. RESULTS Twenty-four articles were included. Mohs surgeons vary in how they assess clinical margins, how wide a margin they excise on the first MMS layer, and how they process tissue to determine tumor stage and margin clearance during MMS for melanoma. CONCLUSION Mohs micrographic surgery for melanoma is performed with varied surgical techniques. To establish best practices, additional research is necessary to determine how different techniques affect outcomes.

Abstract Architectural proteins alter the shape of DNA. Some distort the double helix by introducing sharp kinks. This can serve to relieve strain in tightly-bent DNA structures. Here, we design and test artificial architectural proteins based on a sequence-specific Transcription Activator-like Effector (TALE) protein, either alone or fused to a eukaryotic high mobility group B (HMGB) DNA-bending domain. We hypothesized that TALE protein binding would stiffen DNA to bending and twisting, acting as an architectural protein that antagonizes the formation of small DNA loops. In contrast, fusion to an HMGB domain was hypothesized to generate a targeted DNA-bending architectural protein that facilitates DNA looping. We provide evidence from Escherichia coli Lac repressor gene regulatory loops supporting these hypotheses in living bacteria. Both data fitting to a thermodynamic DNA looping model and sophisticated molecular modeling support the interpretation of these results. We find that TALE protein binding inhibits looping by stiffening DNA to bending and twisting, while the Nhp6A domain enhances looping by bending DNA without introducing twisting flexibility. Our work illustrates artificial approaches to sculpt DNA geometry with functional consequences. Similar approaches may be applicable to tune the stability of small DNA loops in eukaryotes.

OBJECTIVE The objective of this study was to understand the natural history of scoliosis in patients with Chiari malformation type I (CM-I) with and without syringomyelia. METHODS A retrospective review of data was conducted. Patients with CM-I were identified from a cohort of 14,118 individuals age 18 years or younger who had undergone MRI over an 11-year period at the University of Michigan. Patients eligible for study inclusion had a coronal curve ≥ 10° on radiography, associated CM-I with or without syringomyelia, and at least 1 year of clinical follow-up prior to any surgery. Curve magnitude at initial diagnosis, prior to posterior fossa decompression (PFD; if applicable), and at the last follow-up (prior to any surgical correction of scoliosis) was recorded, and clinical and radiographic characteristics were noted. The change in curve magnitude by 10° was defined as curve progression (increase by 10°) or regression (decrease by 10°). RESULTS Forty-three patients met the study inclusion criteria and were analyzed. About one-third (35%) of the patients presented with symptoms attributed to their CM-I. The mean degree of scoliosis at presentation was 32.6° ± 17.7°. Twenty-one patients (49%) had an associated syrinx. The mean tonsil position below the level of the foramen magnum was 9.8 ± 5.8 mm. Patients with a syrinx were more likely to have a curve > 20° (86% vs 41%, p = 0.002). Curve magnitude remained stable (≤ ±10°) in 77% of patients (33/43), progressed in 16% (7/43), and regressed in 7% (3/43). Mean age was higher (14.8 ± 0.59 years) among patients with regressed curves (p = 0.026). All regressed curves initially measured ≤ 20° (mean 14° ± 5.3°), and none of the patients with regressed curves had a syrinx. The change in curve magnitude was statistically similar in patients with (7.32° ± 17.7°) and without (5.32° ± 15.8°) a syrinx (p = 0.67). After a mean follow-up of 3.13 ± 2.04 years prior to surgery, 27 patients (63%) ultimately underwent posterior fossa or scoliosis correction surgery. For those who eventually underwent PFD only, the rate of change in curve magnitude prior to surgery was 0.054° ± 0.79°. The rate of change in curve magnitude was statistically similar before (0.054° ± 0.79°) and after (0.042° ± 0.33°) surgery (p = 0.45) for patients who underwent PFD surgery only. CONCLUSIONS The natural history of scoliosis in the presence of CM-I is variable, though most curves remained stable. All curves that regressed were ≤ 20° at initial diagnosis, and most patients in such cases were older at scoliosis diagnosis. Patients who underwent no surgery or PFD only had similar profiles for the change in curve magnitude, which remained relatively stable overall, as compared to patients who underwent PFD and subsequent fusion, who demonstrated curve progression. Among the patients with a syrinx, no curves regressed, most remained stable, and some progressed. Understanding this variability is a first step toward building a prediction model for outcomes for these patients.