Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study.

BACKGROUND AND PURPOSE: Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor detection and tumor pathology classification. MATERIALS AND METHODS: The study cohort comprised 617 children (median age, 92 months; 56% males) from 5 pediatric institutions with posterior fossa tumors: diffuse midline glioma of the pons (n = 122), medulloblastoma (n = 272), pilocytic astrocytoma (n = 135), and ependymoma (n = 88). There were 199 controls. Tumor histology served as ground truth except for diffuse midline glioma of the pons, which was primarily diagnosed by MR imaging. A modified ResNeXt-50-32x4d architecture served as the backbone for a multitask classifier model, using T2-weighted MRIs as input to detect the presence of tumor and predict tumor class. Deep learning model performance was compared against that of 4 radiologists. RESULTS: Model tumor detection accuracy exceeded an AUROC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F1 score of 0.80. The model was most accurate at predicting diffuse midline glioma of the pons, followed by pilocytic astrocytoma and medulloblastoma. Ependymoma prediction was the least accurate. Tumor type classification accuracy and F1 score were higher than those of 2 of the 4 radiologists. CONCLUSIONS: We present a multi-institutional deep learning model for pediatric posterior fossa tumor detection and classification with the potential to augment and improve the accuracy of radiologic diagnosis.

The molecular structure ordering and orientation of the metallophthalocyanine CoPc, ZnPc, CuPc, and MgPc thin layers deposited on silicon substrate, as studied by micro-Raman spectroscopy.

In this article, we present orientation study of metallophthalocyanine (MPcs) (CoPc, ZnPc, CuPc, and MgPc) thin films deposited on silicon substrate. The MPc's thin layers were obtained by the quasi-molecular beam evaporation. The micro-Raman scattering spectra of MPc's thin films were investigated in the spectral range 550-1650 cm-1 using 488 nm excitation wavelength. Raman scattering studies were performed at room temperature before and after annealing process. Annealing process of thin layers was carried out at 200 °C for 6 h. From polarized Raman spectra using surface Raman mapping, the information on polymorphic phase of MPc's layers has been obtained. The chosen Raman modes A1g and B1g are connected with different polymorphic phases of MPc (α and ß form) thin layers. Moreover, the obtained results showed the influence of the annealing process on the ordering of the molecular structure. Following the annealing process, it was observed arrangement of the thin layers structure being revealed in Raman spectra. The obtained results indicate that the annealing process has a significant influence on the structure of thin layers being under study.