Journal Article

DKFZ-2025-02297

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png u-LINNDA: A protocol for user-optimized lymphoma identification through neural network detection aid.

Fischer, M. (First author)DKFZ* ; Maurer, M.DKFZ* ; Peretzke, R.DKFZ* ; Hauptmann, F. M.DKFZ* ; Ben-Salah, C. ; Jakobs, M. ; Kaes, M. ; Karimian-Jazi, K. ; Scherer, M. R.DKFZ* ; Bendszus, M. ; Maier-Hein, K.DKFZ* ; Krieg, S. M. ; Neumann, J.-O. ; Neher, P.DKFZ* ; Naser, P. (Last author)DKFZ*

2025
Cell Press Cambridge, MA

Abstract: Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are aggressive brain tumors that require neurosurgical treatment. For targeted treatment, biopsy and histopathological identification of the tumor entity are necessary. Here, we present a protocol for diagnosing PCNSL using a convolutional neural network (CNN)-based algorithm. We describe steps for installing the u-LINNDA (user-optimized lymphoma identification through neural network detection aid) algorithm, data preparation and preprocessing, and predicting tumor entities using u-LINNDA. We then detail procedures for predicting tumor identity and inspecting the u-LINNDA report.

Keyword(s): Bioinformatics ; Cancer ; Computer sciences ; Health Sciences ; Neuroscience

Note: #EA:E230#LA:E230#

---

Contributing Institute(s):

1. E230 Medizinische Bildverarbeitung (E230)
2. DKTK HD zentral (HD01)

Research Program(s):

1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2025

---

Database coverage:
; ; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; SCOPUS ; Web of Science Core Collection

---