Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review
<i>Background</i>: Posterior fossa tumors (PFTs) are a morbid group of central nervous system tumors that most often present in childhood. While early diagnosis is critical to drive appropriate treatment, definitive diagnosis is currently only achievable through invasive tissue collectio...
| Main Authors: | , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Multidisciplinary Digital Publishing Institute
2022
|
| Online Access: | https://hdl.handle.net/1721.1/146618 |
| _version_ | 1826200795415052288 |
|---|---|
| author | Yearley, Alexander G. Blitz, Sarah E. Patel, Ruchit V. Chan, Alvin Baird, Lissa C. Friedman, Gregory K. Arnaout, Omar Smith, Timothy R. Bernstock, Joshua D. |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Yearley, Alexander G. Blitz, Sarah E. Patel, Ruchit V. Chan, Alvin Baird, Lissa C. Friedman, Gregory K. Arnaout, Omar Smith, Timothy R. Bernstock, Joshua D. |
| author_sort | Yearley, Alexander G. |
| collection | MIT |
| description | <i>Background</i>: Posterior fossa tumors (PFTs) are a morbid group of central nervous system tumors that most often present in childhood. While early diagnosis is critical to drive appropriate treatment, definitive diagnosis is currently only achievable through invasive tissue collection and histopathological analyses. Machine learning has been investigated as an alternative means of diagnosis. In this systematic review and meta-analysis, we evaluated the primary literature to identify all machine learning algorithms developed to classify and diagnose pediatric PFTs using imaging or molecular data. <i>Methods</i>: Of the 433 primary papers identified in PubMed, EMBASE, and Web of Science, 25 ultimately met the inclusion criteria. The included papers were extracted for algorithm architecture, study parameters, performance, strengths, and limitations. <i>Results</i>: The algorithms exhibited variable performance based on sample size, classifier(s) used, and individual tumor types being investigated. Ependymoma, medulloblastoma, and pilocytic astrocytoma were the most studied tumors with algorithm accuracies ranging from 37.5% to 94.5%. A minority of studies compared the developed algorithm to a trained neuroradiologist, with three imaging-based algorithms yielding superior performance. Common algorithm and study limitations included small sample sizes, uneven representation of individual tumor types, inconsistent performance reporting, and a lack of application in the clinical environment. <i>Conclusions</i>: Artificial intelligence has the potential to improve the speed and accuracy of diagnosis in this field if the right algorithm is applied to the right scenario. Work is needed to standardize outcome reporting and facilitate additional trials to allow for clinical uptake. |
| first_indexed | 2024-09-23T11:41:55Z |
| format | Article |
| id | mit-1721.1/146618 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:41:55Z |
| publishDate | 2022 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1466182023-07-05T17:33:11Z Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review Yearley, Alexander G. Blitz, Sarah E. Patel, Ruchit V. Chan, Alvin Baird, Lissa C. Friedman, Gregory K. Arnaout, Omar Smith, Timothy R. Bernstock, Joshua D. Massachusetts Institute of Technology. Department of Mechanical Engineering <i>Background</i>: Posterior fossa tumors (PFTs) are a morbid group of central nervous system tumors that most often present in childhood. While early diagnosis is critical to drive appropriate treatment, definitive diagnosis is currently only achievable through invasive tissue collection and histopathological analyses. Machine learning has been investigated as an alternative means of diagnosis. In this systematic review and meta-analysis, we evaluated the primary literature to identify all machine learning algorithms developed to classify and diagnose pediatric PFTs using imaging or molecular data. <i>Methods</i>: Of the 433 primary papers identified in PubMed, EMBASE, and Web of Science, 25 ultimately met the inclusion criteria. The included papers were extracted for algorithm architecture, study parameters, performance, strengths, and limitations. <i>Results</i>: The algorithms exhibited variable performance based on sample size, classifier(s) used, and individual tumor types being investigated. Ependymoma, medulloblastoma, and pilocytic astrocytoma were the most studied tumors with algorithm accuracies ranging from 37.5% to 94.5%. A minority of studies compared the developed algorithm to a trained neuroradiologist, with three imaging-based algorithms yielding superior performance. Common algorithm and study limitations included small sample sizes, uneven representation of individual tumor types, inconsistent performance reporting, and a lack of application in the clinical environment. <i>Conclusions</i>: Artificial intelligence has the potential to improve the speed and accuracy of diagnosis in this field if the right algorithm is applied to the right scenario. Work is needed to standardize outcome reporting and facilitate additional trials to allow for clinical uptake. 2022-11-28T14:33:23Z 2022-11-28T14:33:23Z 2022-11-15 2022-11-24T14:43:08Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/146618 Cancers 14 (22): 5608 (2022) PUBLISHER_CC http://dx.doi.org/10.3390/cancers14225608 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Yearley, Alexander G. Blitz, Sarah E. Patel, Ruchit V. Chan, Alvin Baird, Lissa C. Friedman, Gregory K. Arnaout, Omar Smith, Timothy R. Bernstock, Joshua D. Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review |
| title | Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review |
| title_full | Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review |
| title_fullStr | Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review |
| title_full_unstemmed | Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review |
| title_short | Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review |
| title_sort | machine learning in the classification of pediatric posterior fossa tumors a systematic review |
| url | https://hdl.handle.net/1721.1/146618 |
| work_keys_str_mv | AT yearleyalexanderg machinelearningintheclassificationofpediatricposteriorfossatumorsasystematicreview AT blitzsarahe machinelearningintheclassificationofpediatricposteriorfossatumorsasystematicreview AT patelruchitv machinelearningintheclassificationofpediatricposteriorfossatumorsasystematicreview AT chanalvin machinelearningintheclassificationofpediatricposteriorfossatumorsasystematicreview AT bairdlissac machinelearningintheclassificationofpediatricposteriorfossatumorsasystematicreview AT friedmangregoryk machinelearningintheclassificationofpediatricposteriorfossatumorsasystematicreview AT arnaoutomar machinelearningintheclassificationofpediatricposteriorfossatumorsasystematicreview AT smithtimothyr machinelearningintheclassificationofpediatricposteriorfossatumorsasystematicreview AT bernstockjoshuad machinelearningintheclassificationofpediatricposteriorfossatumorsasystematicreview |