Craniopharyngiomas are locally-aggressive tumors arising along the hypothalamic-pituitary axis. Treatment is nuanced as a result of their proximity and adherence to vital neurovascular structures and responsiveness to surgery, radiation and, in some cases, chemotherapy. We reviewed the literature discussing the current state of knowledge regarding craniopharyngioma biology and therapy. Recent advances in endoscopic endonasal surgery (EEA) have made surgery a safer and more effective option. While cure may be achieved with gross total resection (GTR), when felt to be too risky, a subtotal resection followed by radiation is often a more prudent strategy, particularly in children with hypothalamic invasion. Data on long-term outcome are mostly derived from older studies in which a craniotomy, rather than EEA, was performed. Long-term EEA outcome studies are lacking. Enhanced knowledge of the biological basis of papillary CPs has led to novel medical treatments for BRAFv600E variants that appear to be effective. Endoscopic technology has improved surgical results for craniopharyngiomas and expanded the indications for the transsphenoidal approach. The goal of CP surgery goal is maximal safe resection to achieve cure, but subtotal resection and radiation may be equally effective. Early diagnosis of specific variants will facilitate enrollment in promising medical trials.

Update on management of craniopharyngiomas

Abstract Objectives To develop a U-Net-based deep learning model for automated segmentation of craniopharyngioma. Methods A total number of 264 patients diagnosed with craniopharyngiomas were included in this research. Pre-treatment MRIs were collected, annotated, and used as ground truth to learn and evaluate the deep learning model. Thirty-eight patients from another institution were used for independently external testing. The proposed segmentation model was constructed based on a U-Net architecture. Dice similarity coefficients (DSCs), Hausdorff distance of 95% percentile (95HD), Jaccard value, true positive rate (TPR), and false positive rate (FPR) of each case were calculated. One-way ANOVA analysis was used to investigate if the model performance was associated with the radiological characteristics of tumors. Results The proposed model showed a good performance in segmentation with average DSCs of 0.840, Jaccard of 0.734, TPR of 0.820, FPR of 0.000, and 95HD of 3.669 mm. It performed feasibly in the independent external test set, with average DSCs of 0.816, Jaccard of 0.704, TPR of 0.765, FPR of 0.000, and 95HD of 4.201 mm. Also, one-way ANOVA suggested the performance was not statistically associated with radiological characteristics, including predominantly composition ( p = 0.370), lobulated shape ( p = 0.353), compressed or enclosed ICA ( p = 0.809), and cavernous sinus invasion ( p = 0.283). Conclusions The proposed deep learning model shows promising results for the automated segmentation of craniopharyngioma. Key Points • The segmentation model based on U-Net showed good performance in segmentation of craniopharyngioma. • The proposed model showed good performance regardless of the radiological characteristics of craniopharyngioma. • The model achieved feasibility in the independent external dataset obtained from another center. 

/pdf/automated-segmentation-of-craniopharyngioma-on-mr-images-jy4d104v.pdf

Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network

Purpose: The goal of this study was to develop end-to-end convolutional neural network (CNN) models that can noninvasively discriminate papillary craniopharyngioma (PCP) from adamantinomatous craniopharyngioma (ACP) on MR images requiring no manual segmentation. Materials and methods: A total of 97 patients diagnosed with ACP or PCP were included. Pretreatment contrast-enhanced T1-weighted images were collected and used as the input of the CNNs. Six models were established based on six networks, including VGG16, ResNet18, ResNet50, ResNet101, DenseNet121, and DenseNet169. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performances of these deep neural networks. A five-fold cross-validation was applied to evaluate the performances of the models. Results: The six networks yielded feasible performances, with area under the receiver operating characteristic curves (AUCs) of at least 0.78 for classification. The model based on Resnet50 achieved the highest AUC of 0.838 ± 0.062, with an accuracy of 0.757 ± 0.052, a sensitivity of 0.608 ± 0.198, and a specificity of 0.845 ± 0.034, respectively. Moreover, the results also indicated that the CNN method had a competitive performance compared to the radiomics-based method, which required manual segmentation for feature extraction and further feature selection. Conclusions: MRI-based deep neural networks can noninvasively differentiate ACP from PCP to facilitate the personalized assessment of craniopharyngiomas.

https://www.mdpi.com/2077-0383/11/24/7481/pdf?version=1671599420

Pathological Diagnosis of Adult Craniopharyngioma on MR Images: An Automated End-to-End Approach Based on Deep Neural Networks Requiring No Manual Segmentation

Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

/pdf/beyond-glioma-the-utility-of-radiomic-analysis-for-non-glial-409zt8qb.pdf

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Dabrafenib as a diagnostic and therapeutic strategy for the non-surgical management of papillary craniopharyngioma.

Background Preoperative, noninvasive discrimination of the craniopharyngioma subtypes is important because it influences the treatment strategy. Purpose To develop a radiomic model based on multiparametric magnetic resonance imaging for noninvasive discrimination of pathological subtypes of craniopharyngioma. Study type Retrospective. Population A total of 164 patients from two medical centers were enrolled in this study. Patients from the first medical center were divided into a training cohort (N = 99) and an internal validation cohort (N = 33). Patients from the second medical center were used as the external independent validation cohort (N = 32). Field strength/sequence Axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w) on 3.0 T or 1.5 T magnetic resonance scanners. Assessment Pathological subtypes (squamous papillary craniopharyngioma and adamantinomatous craniopharyngioma) were confirmed by surgery and hematoxylin and eosin staining. Optimal radiomic feature selection was performed by SelectKBest, the least absolute shrinkage and selection operator algorithm, and support vector machine (SVM) with a recursive feature elimination algorithm. Models based on each sequence or combinations of sequences were built using a SVM classifier and used to differentiate pathological subtypes of craniopharyngioma in the training cohort, internal validation, and external validation cohorts. Statistical tests The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the radiomic models. Results Seven texture features, three from T1 -w, two from T2 -w, and two from CET1 -w, were selected and used to construct the radiomic model. The AUC values of the radiomic model were 0.899, 0.810, and 0.920 in the training cohort, internal and external validation cohorts, respectively. The AUC values of the clinicoradiological model were 0.677, 0.655, and 0.671 in the training cohort, internal and external validation cohorts, respectively. Data conclusion The model based on radiomic features from T1 -w, T2 -w, and CET1 -w has a high discriminatory ability for pathological subtypes of craniopharyngioma. Level of evidence 4 TECHNICAL EFFICACY: 2.

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma

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Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma