Simple Ways to Estimate Meningioma Volume: Can ABC- and SH-Derived Methods Be Used in Clinical Practice Reliably?

Unexpected reaction of "wild-type" gastrointestinal stromal tumor to imatinib: case report and literature review

Background

Most of gastrointestinal stromal tumors (GISTs) are driven by mutations in the KIT/PDGFRA genes and can benefit from TKIs treatment. However, a small subset of GIST (10%-15%) are called "wild-type" GISTs due to the lack of these mutations. Theoretically, they would not benefit from TKIs treatment and may even develop resistance. Therefore, this unexpected response may challenge inherent perceptions. Herein, we present a case of giant wild-type GIST exhibiting an unexpected response to imatinib(IM), followed by laparoscopic surgical resection. Subsequently, potential underlying mechanisms are discussed.

Case description

This case describes a 57-year-old man who presented with abdominal pain for two weeks. CT revealed a massive lesion near the splenic hilum along the greater curvature of the stomach, concurrently involving the splenic hilar vessels and surrounding lymph nodes. Ultrasound-guided fine needle aspiration biopsy confirmed it is a mesenchymal spindle cell tumor,GIST. Due to the enormous volume and local invasion, neoadjuvant chemotherapy was initially considered. After 6 months of IM 400 mg/d, CT imaging revealed marked changes in tumor heterogeneity and a significant reduction in volume. Subsequently, laparoscopic surgical resection was performed. Postoperative pathological examination, immunohistochemistry, and genetic testing collectively confirmed it is a wild-type GIST.The patient recovered well and was discharged on the 6th day after surgery, with continued oral IM(400 mg/d) after discharge. No recurrence was observed during follow-up until the publication of this report.

Conclusion

This unexpected response suggests that wild-type GISTs may benefit from TKIs treatment, and the potential mechanisms warrant further investigation. Additionally, true wild-type GIST may not be discerned due to current limitations of Next-Generation Sequencing(NGS). Therefore, for advanced/high-risk GIST, additional genetic analysis can be performed after negative NGS results.

GAB1 as a Marker of Recurrence in Anterior Skull Base Meningioma

BACKGROUND

About one-third of anterior skull base meningiomas show Hedgehog pathway activation. We have recently identified GAB1 as a surrogate marker for Hedgehog pathway-activated meningiomas.

OBJECTIVE

To determine the reproducibility and prognostic value of GAB1 marker in anterior skull base meningiomas.

METHODS

A retrospective bicentric cohort of anterior skull base meningiomas, operated from 2005 to 2015, was constituted. GAB1 immunohistochemistry was performed in 2 centers, and the GAB1 score was assessed. Clinical and pathological data were reviewed to determine the prognostic value of the GAB1 score, along with classical factors of recurrence.

RESULTS

One hundred forty-eight patients were included (median follow-up of 72 ± 46 months). 78% of patients had gross total resection. Eighty-four percentage of patients harbored grade 1 meningiomas. GAB1 immunohistochemistry was positive (ie, GAB1 staining score was >250) in 53 cases (35%). GAB1-positive cases were mainly at olfactory groove, of meningothelial grade 1 subtype, and showed greater recurrence (36% vs 14%, P = .002), greater requirement for multiple surgeries (17% vs 4.2%, P = .014), and more likely evolution toward diffuse skull base infiltration (15% vs 3%, P = .0017). By multivariable Cox regression analysis, incomplete surgical resection (hazard ratios [HR] = 8.3, 95% IC [3.7-18.2], P < .001), male sex (HR = 5.4, 95% IC [2.2-13.5], P < .001), GAB1 positivity (HR = 3.2, 95% CI [1.5-6.9], P = .004), and Ki67 index >4 (HR = 2.2, 95% IC [1.2-4.6], P = .035) were independent prognostic factors for recurrence.

CONCLUSION

GAB1 marker is an independent prognostic factor for anterior skull base meningioma and could be useful for both prognostic evaluation and identification of Hedgehog-activated meningiomas.

Evaluation of Traumatic Subdural Hematoma Volume by Using Image Segmentation Assessment Based on Deep Learning

Rapid and accurate evaluations of hematoma volume can guide the treatment of traumatic subdural hematoma. We aim to explore the consistency between the measurement results of traumatic subdural hematoma (TSDH) using a deep learn-based image segmentation algorithm. A retrospective study was conducted on 90 CT images of patients diagnosed with TSDH in our hospital from January 2019 to January 2022. All image data were measured by manual segmentation, convolutional neural networks (CNN) algorithm segmentation, and ABC/2 volume formula. With manual segmentation as the “golden standard,” a consistency test was carried out with CNN algorithm segmentation and ABC/2 volume formula, respectively. The percentage error of CNN algorithm segmentation is less than ABC/2 volume formula. There is no significant difference between CNN algorithm segmentation and manual segmentation (P > 0.05). The area under curve of the ABC/2 volume formula, manual segmentation, and CNN algorithm segmentation is 0.811 (95% CI: 0.717~0.905), 0.840 (95% CI: 0.753~0.928), and 0.832 (95% CI: 0.742~0.922), respectively. From our results, the algorithm based on CNN has a good efficiency in segmentation and accurate calculation of TSDH hematoma volume.