Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study.
Simple Summary: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, due to important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not been tested to date to distinguish be...
| Publicado en: | Cancers Vol. 16; no. 14; pp. 2578 - 2594 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
MDPI
Jul2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178701220&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178701220 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jul2024 vid: 16 iid: 14 pid: 97109 pub: MDPI artinfo: ui: 178701220 178701220 178701220 10.3390/cancers16142578 178701220 ppf: 2578 ppct: 16 formats: tig: atl: Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study. aug: au: Voicu, Ioan Paul Dotta, Francesco Napolitano, Antonio Caulo, Massimo Piccirilli, Eleonora D'Orazio, Claudia Carai, Andrea Miele, Evelina Vinci, Maria Rossi, Sabrina Cacchione, Antonella Vennarini, Sabina Del Baldo, Giada Mastronuzzi, Angela Tomà, Paolo Colafati, Giovanna Stefania affil: Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children's Hospital, IRCCS, 00165 Rome, Italy sug: subj: Machine Learning Evaluation Magnetic Resonance Imaging Methods Infratentorial Neoplasms Therapy Infratentorial Neoplasms Prognosis Pediatric Care Human Male Female Child Pilot Studies Retrospective Design Random Forest Observer Bias Glioma Cancer Patients Psychosocial Factors Descriptive Statistics Comparative Studies Child: 6-12 years Male Female ab: Simple Summary: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, due to important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not been tested to date to distinguish between pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve the repeatability of diffusion measurements compared to conventional region-of-interest (ROI) approaches. Our purpose was twofold: to assess the repeatability of VOI DKI-derived diffusion measurements and DKI accuracy in discriminating among pediatric PF tumors, by employing conventional statistical analyses and machine learning (ML) techniques. Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffusion measurements and assess DKI accuracy in discriminating among pediatric PF tumors. Materials and methods: We retrospectively analyzed 34 children (M, F, mean age 7.48 years) with PF tumors who underwent preoperative examination on a 3 Tesla magnet, including DKI. For each patient, two neuroradiologists independently segmented the whole solid tumor, the ROI of the area of maximum tumor diameter, and a small 5 mm ROI. The automated analysis pipeline included inter-observer variability, statistical, and machine learning (ML) analyses. We evaluated inter-observer variability with coefficient of variation (COV) and Bland–Altman plots. We estimated DKI metrics accuracy in discriminating among tumor histology with MANOVA analysis. In order to account for class imbalances, we applied SMOTE to balance the dataset. Finally, we performed a Random Forest (RF) machine learning classification analysis based on all DKI metrics from the SMOTE dataset by partitioning 70/30 the training and testing cohort. Results: Tumor histology included medulloblastoma (15), pilocytic astrocytoma (14), and ependymoma (5). VOI-based measurements presented lower variability than ROI-based measurements across all DKI metrics and were used for the analysis. DKI-derived metrics could accurately discriminate between tumor subtypes (Pillai's trace: p < 0.001). SMOTE generated 11 synthetic observations (10 EP and 1 PA), resulting in a balanced dataset with 45 instances (34 original and 11 synthetic). ML analysis yielded an accuracy of 0.928, which correctly predicted all but one lesion in the testing set. Conclusions: VOI-based measurements presented improved repeatability compared to ROI-based measurements across all diffusion metrics. An ML classification algorithm resulted accurate in discriminating PF tumors on a SMOTE-generated dataset. ML techniques based on DKI-derived metrics are useful for the discrimination of pediatric PF tumors. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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