MSTPublications: September 2023
Artificial Intelligence to Preoperatively Predict Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery: Soft Tissue Imaging May be Necessary for Accurate Models.
Johnson GW, Chanbour H, Ali MA, Chen J, Metcalf T, Doss D, Younus I, Jonzzon S, Roth SG, Abtahi AM, Stephens BF, Zuckerman SL.
Spine (Phila Pa 1976). 2023 Aug 30. doi: 10.1097/BRS.0000000000004816. Online ahead of print.
Study design: Retrospective cohort.
Objective: In a cohort of patients undergoing adult spinal deformity (ASD) surgery, we used artificial intelligence to compare three models of preoperatively predicting radiographic proximal junction kyphosis (PJK) using: 1) traditional demographics and radiographic measurements, 2) raw preoperative scoliosis radiographs, and 3) raw preoperative thoracic magnetic resonance imaging (MRI).
Summary of background data: Despite many proposed risk factors, PJK following ASD surgery remains difficult to predict.
Methods: A single-institution, retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009-21. PJK was defined as a sagittal Cobb angle of upper-instrumented vertebra (UIV) and UIV+2>10° and a postoperative change in UIV/UIV+2>10°. For Model-1, a support vector machine was used to predict PJK within 2 years postoperatively using clinical and traditional sagittal/coronal radiographic variables and intended levels of instrumentation. Next, for Model-2, a convolutional neural network (CNN) was trained on raw preoperative lateral and posterior-anterior scoliosis radiographs. Finally, for Model-3, a CNN was trained on raw preoperative thoracic T1 MRIs.
Results: A total of 191 patients underwent ASD surgery with at least 2-year follow-up and 89 (46.6%) developed radiographic PJK within 2 years. Model-1: Using clinical variables and traditional radiographic measurements, the model achieved a sensitivity:57.2% and specificity:56.3%. Model-2: a CNN with raw scoliosis x-rays predicted PJK with sensitivity: 68.2% and specificity: 58.3%. Model-3: a CNN with raw thoracic MRIs predicted PJK with average sensitivity: 73.1% and specificity: 79.5%. Finally, an attention map outlined the imaging features used by Model-3 elucidated that soft tissue features predominated all true positive PJK predictions.
Conclusion: The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared to raw scoliosis radiographs and traditional clinical/radiographic measurements. The improved predictive accuracy using MRI may indicate that PJK is best predicted by soft-tissue degeneration and muscle atrophy.
Molecular signature incorporating the immune microenvironment enhances thyroid cancer outcome prediction.
Xu, GJ, Loberg, MA, Gallant, J-N, Sheng, Q, Chen, S-C, Lehmann, BD, Shaddy, SM, Tigue, ML, Phifer, CJ, Wang, L, Saab-Chalhoub, MW, Dehan, LM, Wei, Q, Chen, R, Li, B, Kim, CY, Ferguson, DC, Netterville, JL, Rohde, SL, Solórzano, CC, Bischoff, LA, Baregamian, N, Shaver, AC, Mehrad, M, Ely, KA, Byrne, DW, Stricker, TP, Murphy, BA, Choe, JH, Kagohara, LT, Jaffee, EM, Huang, EC, Ye, F, Lee, E, Weiss, VL,
Cell Genomics. 2023 Sept 14. doi: 10.1016/j.xgen.2023.100409.
Genomic and transcriptomic analysis has furthered our understanding of many tumors. Yet, thyroid cancer management is largely guided by staging and histology, with few molecular prognostic and treatment biomarkers. Here, we utilize a large cohort of 251 patients with 312 samples from two tertiary medical centers and perform DNA/RNA sequencing, spatial transcriptomics, and multiplex immunofluorescence to identify biomarkers of aggressive thyroid malignancy. We identify high-risk mutations and discover a unique molecular signature of aggressive disease, the Molecular Aggression and Prediction (MAP) score, which provides improved prognostication over high-risk mutations alone. The MAP score is enriched for genes involved in epithelial de-differentiation, cellular division, and the tumor microenvironment. The MAP score also identifies aggressive tumors with lymphocyte-rich stroma that may benefit from immunotherapy. Future clinical profiling of the stromal microenvironment of thyroid cancer could improve prognostication, inform immunotherapy, and support development of novel therapeutics for thyroid cancer and other stroma-rich tumors.
Examining the latent structure and correlates of sensory reactivity in autism: a multi-site integrative data analysis by the autism sensory research consortium.
Williams ZJ, Schaaf R, Ausderau KK, Baranek GT, Barrett DJ, Cascio CJ, Dumont RL, Eyoh EE, Failla MD, Feldman JI, Foss-Feig JH, Green HL, Green SA, He JL, Kaplan-Kahn EA, Keçeli-Kaysılı B, MacLennan K, Mailloux Z, Marco EJ, Mash LE, McKernan EP, Molholm S, Mostofsky SH, Puts NAJ, Robertson CE, Russo N, Shea N, Sideris J, Sutcliffe JS, Tavassoli T, Wallace MT, Wodka EL, Woynaroski TG.
Mol Autism. 2023 Aug 28;14(1):31. doi: 10.1186/s13229-023-00563-4.
Background: Differences in responding to sensory stimuli, including sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and sensory seeking (SEEK) have been observed in autistic individuals across sensory modalities, but few studies have examined the structure of these “supra-modal” traits in the autistic population.
Methods: Leveraging a combined sample of 3868 autistic youth drawn from 12 distinct data sources (ages 3-18 years and representing the full range of cognitive ability), the current study used modern psychometric and meta-analytic techniques to interrogate the latent structure and correlates of caregiver-reported HYPER, HYPO, and SEEK within and across sensory modalities. Bifactor statistical indices were used to both evaluate the strength of a “general response pattern” factor for each supra-modal construct and determine the added value of “modality-specific response pattern” scores (e.g., Visual HYPER). Bayesian random-effects integrative data analysis models were used to examine the clinical and demographic correlates of all interpretable HYPER, HYPO, and SEEK (sub)constructs.
Results: All modality-specific HYPER subconstructs could be reliably and validly measured, whereas certain modality-specific HYPO and SEEK subconstructs were psychometrically inadequate when measured using existing items. Bifactor analyses supported the validity of a supra-modal HYPER construct (ωH = .800) but not a supra-modal HYPO construct (ωH = .653), and supra-modal SEEK models suggested a more limited version of the construct that excluded some sensory modalities (ωH = .800; 4/7 modalities). Modality-specific subscales demonstrated significant added value for all response patterns. Meta-analytic correlations varied by construct, although sensory features tended to correlate most with other domains of core autism features and co-occurring psychiatric symptoms (with general HYPER and speech HYPO demonstrating the largest numbers of practically significant correlations).
Limitations: Conclusions may not be generalizable beyond the specific pool of items used in the current study, which was limited to caregiver report of observable behaviors and excluded multisensory items that reflect many “real-world” sensory experiences.
Conclusion: Of the three sensory response patterns, only HYPER demonstrated sufficient evidence for valid interpretation at the supra-modal level, whereas supra-modal HYPO/SEEK constructs demonstrated substantial psychometric limitations. For clinicians and researchers seeking to characterize sensory reactivity in autism, modality-specific response pattern scores may represent viable alternatives that overcome many of these limitations.
The INSAR Community Collaborator Request: Using community-academic partnerships to enhance outcomes of participatory autism research.
Poulsen R, Dwyer P, Gassner D, Heyworth M, Williams ZJ.
Autism Res. 2023 Sep 9. doi: 10.1002/aur.3027. Online ahead of print.
Participatory approaches, in which researchers work together with members of the autism community (e.g., autistic people, family members, caregivers, or other stakeholders) to design, conduct, and disseminate research, have become increasingly prominent within the field of autism research over the past decade. Despite growing academic and community interest in conducting participatory studies, stakeholder collaboration remains infrequent in autism research, at least partially due to systemic barriers. To help reduce barriers to engaging in participatory autism research, the International Society for Autism Research (INSAR) Autistic Researchers Committee has launched the INSAR Community Collaborator Request (ICCR; https://www.autism-insar.org/page/iccr), a platform on the INSAR website that allows autism researchers conducting participatory research to seek out stakeholder collaborators from the autism community (including both autistic people and their family members/caregivers, as relevant to a given research project). Interested stakeholders also have the opportunity to subscribe to ICCR posts, allowing them to be alerted of new opportunities for collaboration and potentially increasing their involvement in autism research. Overall, the ICCR provides a venue to connect autism researchers with potential community collaborators, reducing barriers to participatory autism research and increasing the frequency of successful community-academic partnerships within the field. We are hopeful that in the long term, such changes will lead to greater alignment between research outputs and the goals of the greater autism community, and consequently an increase in the overall quality and relevance of autism research.
UNesT: Local spatial representation learning with hierarchical transformer for efficient medical segmentation.
Yu X, Yang Q, Zhou Y, Cai LY, Gao R, Lee HH, Li T, Bao S, Xu Z, Lasko TA, Abramson RG, Zhang Z, Huo Y, Landman BA, Tang Y.
Med Image Anal. 2023 Aug 25;90:102939. doi: 10.1016/j.media.2023.102939. Online ahead of print.
Intraoperative physiology augments atlas-based data in awake deep brain stimulation.
Paulo DL, Johnson GW, Doss DJ, Allen JH, González HFJ, Shults R, Li R, Ball TJ, Bick SK, Hassell TJ, D’Haese PF, Konrad PE, Dawant BM, Narasimhan S, Englot DJ.
J Neurol Neurosurg Psychiatry. 2023 Sep 7:jnnp-2023-331248. doi: 10.1136/jnnp-2023-331248. Online ahead of print.
Corticostriatal beta oscillation changes associated with cognitive function in Parkinson’s disease.
Paulo DL, Qian H, Subramanian D, Johnson GW, Zhao Z, Hett K, Kang H, Chris Kao C, Roy N, Summers JE, Claassen DO, Dhima K, Bick SK.
Brain. 2023 Sep 1;146(9):3662-3675. doi: 10.1093/brain/awad206.
Structural disconnection relates to functional changes after temporal lobe epilepsy surgery.
Sainburg LE, Janson AP, Johnson GW, Jiang JW, Rogers BP, Chang C, Englot DJ, Morgan VL.
Brain. 2023 Sep 1;146(9):3913-3922. doi: 10.1093/brain/awad117.
Abnormal functional connectivity of the posterior hypothalamus and other arousal regions in surgical temporal lobe epilepsy.
Jiang JW, Narasimhan S, Johnson GW, González HFJ, Doss DJ, Shless JS, Paulo DL, Terry DP, Chang C, Morgan VL, Englot DJ.
J Neurosurg. 2023 Feb 17;139(3):640-650. doi: 10.3171/2023.1.JNS221452. Print 2023 Sep 1.
Hurst MN, Beebout CJ, Hollingsworth A, Guckes KR, Purcell A, Bermudez TA, Williams D, Reasoner SA, Trent MS, Hadjifrangiskou M.
mSphere. 2023 Sep 7:e0005923. doi: 10.1128/msphere.00059-23. Online ahead of print.
Habituation of auditory responses in young autistic and neurotypical children.
Dwyer P, Williams ZJ, Vukusic S, Saron CD, Rivera SM.
Autism Res. 2023 Sep 9. doi: 10.1002/aur.3022. Online ahead of print.
Clostridioides difficile Infection in Pediatric Inflammatory Bowel Disease.
Reasoner SA, Nicholson MR.
Curr Gastroenterol Rep. 2023 Aug 30. doi: 10.1007/s11894-023-00890-9. Online ahead of print.
Uterine leiomyomata and keloids fibrosis origins: A mini-review of fibroproliferative diseases.
Hampton G, Kim J, Edwards T, Hellwege J, Velez Edwards DR.
Am J Physiol Cell Physiol. 2023 Aug 29. doi: 10.1152/ajpcell.00181.2023. Online ahead of print.