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Literature DB >> 35029678

BPNSTs: In the eye of the beholder.

Timothy J Kaufmann¹, Bradley J Erickson¹.

Abstract

Entities: Chemical

Mesh：

Year: 2022 PMID： 35029678 PMCID： PMC8972269 DOI： 10.1093/neuonc/noab296

Source DB: PubMed Journal: Neuro Oncol ISSN： 1522-8517 Impact factor: 12.300

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References

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8 in total

Review 1. The target sign: extremity.

Authors: Kevin P Banks
Journal: Radiology Date: 2005-03 Impact factor: 11.105

2. The Risk of Peripheral Nerve Tumor Biopsy in Suspected Benign Etiologies.

Authors: Roberto J Perez-Roman; S Shelby Burks; Luca Debs; Iahn Cajigas; Allan D Levi
Journal: Neurosurgery Date: 2020-03-01 Impact factor: 4.654

3. Artificial Intelligence in Neuroradiology: Current Status and Future Directions.

Authors: Y W Lui; P D Chang; G Zaharchuk; D P Barboriak; A E Flanders; M Wintermark; C P Hess; C G Filippi
Journal: AJNR Am J Neuroradiol Date: 2020-07-30 Impact factor: 3.825

4. MR imaging differentiation of benign and malignant peripheral nerve sheath tumors: use of the target sign.

Authors: R Bhargava; D M Parham; O E Lasater; R S Chari; G Chen; B D Fletcher
Journal: Pediatr Radiol Date: 1997-02

5. Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: A multi-center study.

Authors: Michael Zhang; Elizabeth Tong; Sam Wong; Forrest Hamrick; Maryam Mohammadzadeh; Vaishnavi Rao; Courtney Pendleton; Brandon W Smith; Nicholas F Hug; Sandip Biswal; Jayne Seekins; Sandy Napel; Robert J Spinner; Mark A Mahan; Kristen W Yeom; Thomas J Wilson
Journal: Neuro Oncol Date: 2022-04-01 Impact factor: 13.029

BPNSTs: In the eye of the beholder.

Review 1. The target sign: extremity.

2. The Risk of Peripheral Nerve Tumor Biopsy in Suspected Benign Etiologies.

3. Artificial Intelligence in Neuroradiology: Current Status and Future Directions.

4. MR imaging differentiation of benign and malignant peripheral nerve sheath tumors: use of the target sign.

5. Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: A multi-center study.

Review 6. Predicting cancer outcomes with radiomics and artificial intelligence in radiology.

7. Clinicoradiological features of intraneural perineuriomas obviate the need for tissue diagnosis.

8. Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges.