Metastasis of renal clear cell carcinoma to the spinal cord are quite rare. Intradural localization causing a cauda equina syndrome has been previously reported only in two cases. The present report details the clinical, surgical and neuroradiological findings of a third case requiring emergency surgery, and presents data available from a brief review of cases reported in the literature. From the data available in the literature, we suggest that cerebral and spinal MRI and PET imaging should be widely performed in the staging of patients treated for renal clear cell carcinoma, in order to early detect CNS involvement.
✓Guido da Vigevano was an Italian physician and engineer who lived in the 13th and 14th centuries. He was the first scientist who used pictures to illustrate his anatomical descriptions, developing for the first time a close relationship between anatomical studies and artistic drawings. This was further developed in the Renaissance.In his textbook Anathomia are displayed six plates showing for the first time neuroanatomical structures and techniques: dissection of the head by means of trephination, and depictions of the meninges, cerebrum, and spinal cord. On the surface of the brain painting it is possible to recognize a vague patterning of cortical convolutions. Ventricles are also described and shown. This book constituted the first attempt in the history of neuroscience to illustrate an anatomical description with schematic pictures to achieve a better understanding of such complex structures.
Context.—
Glioma is the most common primary brain tumor in adults. The diagnosis and grading of different pathological subtypes of glioma is essential in treatment planning and prognosis.
Objective.—
To propose a deep learning–based approach for the automated classification of glioma histopathology images. Two classification methods, the ensemble method based on 2 binary classifiers and the multiclass method using a single multiclass classifier, were implemented to classify glioma images into astrocytoma, oligodendroglioma, and glioblastoma, according to the 5th edition of the World Health Organization classification of central nervous system tumors, published in 2021.
Design.—
We tested 2 different deep neural network architectures (VGG19 and ResNet50) and extensively validated the proposed approach based on The Cancer Genome Atlas data set (n = 700). We also studied the effects of stain normalization and data augmentation on the glioma classification task.
Results.—
With the binary classifiers, our model could distinguish astrocytoma and oligodendroglioma (combined) from glioblastoma with an accuracy of 0.917 (area under the curve [AUC] = 0.976) and astrocytoma from oligodendroglioma (accuracy = 0.821, AUC score = 0.865). The multiclass method (accuracy = 0.861, AUC score = 0.961) outperformed the ensemble method (accuracy = 0.847, AUC = 0.933) with the best performance displayed by the ResNet50 architecture.
Conclusions.—
With the high performance of our model (>80%), the proposed method can assist pathologists and physicians to support examination and differential diagnosis of glioma histopathology images, with the aim to expedite personalized medical care.
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