The Shape grading system: a classification for growth patterns of pituitary adenomas

Predictive value of cyst/tumor volume ratio of pituitary adenoma for tumor cell proliferation

BACKGROUND

MRI has been widely used to predict the preoperative proliferative potential of pituitary adenoma (PA). However, the relationship between the cyst/tumor volume ratio (C/T ratio) and the proliferative potential of PA has not been reported. Herein, we determined the predictive value of the C/T ratio of PA for tumor cell proliferation.

METHODS

The clinical data of 72 patients with PA and cystic change on MRI were retrospectively analyzed. PA volume, cyst volume, and C/T ratio were calculated. The corresponding intraoperative specimens were collected. Immunohistochemistry and hematoxylin-eosin staining were performed to evaluate the Ki67 index and nuclear atypia. Patients were categorized according to the Ki67 index (< 3% and ≥ 3%) and nuclear atypia (absence and presence). Univariate and multivariate analyses were used to identify the significant predictors of the Ki67 index and nuclear atypia. The receiver operating characteristic curve assessed the prediction ability of the significant predictors.

RESULTS

Larger tumor volumes, smaller cyst volumes, and lower C/T ratios were found in patients with higher Ki67 indexes and those with nuclear atypia (P < 0.05). C/T ratio was an independent predictor of the Ki67 index (odds ratio = 0.010, 95% confidence interval = 0.000-0.462) and nuclear atypia (odds ratio = 0.010, 95% confidence interval = 0.000-0.250). The predictive value of the C/T ratio did not differ significantly from that of tumor volume (P > 0.05) but was better than that of cyst volume (P < 0.05). The area under the curve of the C/T ratio for predicting the Ki67 index and nuclear atypia was larger than that for predicting cyst volume and tumor volume.

CONCLUSIONS

C/T ratios can be used to predict PA tumor proliferation preoperatively. Our findings may facilitate the selection of surgery timing and the efficacy evaluation of surgery.

Trouillas's Grading and Post-Surgical Tumor Residue Assessment in Pituitary Adenomas: The Importance of the Multidisciplinary Approach

BACKGROUND

We aim to assess the role of a multidisciplinary approach in pituitary adenomas (PitNETs) classification, evaluate criteria concordance, and compare intraoperative assessments with post-operative MRIs for tumor remnants.

METHODS

Clinical, radiological, histological, and intra- and post-operative data of the treated PitNETs were extracted from prospectively created records. PitNETs were graded according to Trouillas, and the evaluation of the tumor remnants was recorded.

RESULTS

Of 362 PitNETs, 306 underwent surgery, with Trouillas grading assigned to 296. Eight-nine radiologically non-invasive PitNETs progressed to grades 1b (27), 2a (42), or 2b (20) due to proliferative or surgical invasiveness criteria. Twenty-six radiologically invasive tumors were graded 2b due to proliferative criteria. Surgical resection details and post-surgical MRI findings revealed that residual tumors were more common in grades 2a and 2b. During surgery, small tumor remnants were documented in 14 patients which were not visible on post-surgical MRI. Post-surgical MRIs identified remnants in 19 PitNETs not seen during surgery, located in lateral recesses of the sella (4), retrosellar (2), or suprasellar regions (7), along the medial wall of the cavernous sinus (6).

CONCLUSIONS

The Pituitary Board allows for the correct grading of PitNETs to be obtained and an accurate identification of high-risk patients who should undergo closer surveillance due to tumor remnants.

Update on Current Evidence for the Diagnosis and Management of Nonfunctioning Pituitary Neuroendocrine Tumors

Pituitary neuroendocrine tumors (PitNETs) are the third most frequently diagnosed intracranial tumors, with nonfunctioning PitNETs (nfPitNETs) accounting for 30% of all pituitary tumors and representing the most common type of macroPitNETs. NfPitNETs are usually benign tumors with no evidence of hormone oversecretion except for hyperprolactinemia secondary to pituitary stalk compression. Due to this, they do not typically present with clinical syndromes like acromegaly, Cushing's disease or hyperthyroidism and instead are identified incidentally on imaging or from symptoms of mass effects (headache, vision changes, apoplexy). With the lack of effective medical interventions, first-line treatment is transsphenoidal surgical resection, however, nfPitNETs often have supra- or parasellar extension, and total resection of the tumor is often not possible, resulting in residual tumor regrowth or reoccurrence. While functional PitNETs can be easily followed for recurrence using hormonal biomarkers, there is no similar parameter to predict recurrence in nfPitNETs, hence delaying early recognition and timely management. Therefore, there is a need to identify prognostic biomarkers that can be used for patient surveillance and as therapeutic targets. This review focuses on summarizing the current evidence on nfPitNETs, with a special focus on potential new biomarkers and therapeutics.

The Party Wall: Redefining the Indications of Transcranial Approaches for Giant Pituitary Adenomas in Endoscopic Era

The evolution of endoscopic trans-sphenoidal surgery raises the question of the role of transcranial surgery for pituitary tumors, particularly with the effectiveness of adjunct irradiation. This narrative review aims to redefine the current indications for the transcranial approaches for giant pituitary adenomas in the endoscopic era. A critical appraisal of the personal series of the senior author (O.A.-M.) was performed to characterize the patient factors and the tumor's pathological anatomy features that endorse a cranial approach. Traditional indications for transcranial approaches include the absent pneumatization of the sphenoid sinus; kissing/ectatic internal carotid arteries; reduced dimensions of the sella; lateral invasion of the cavernous sinus lateral to the carotid artery; dumbbell-shaped tumors caused by severe diaphragm constriction; fibrous/calcified tumor consistency; wide supra-, para-, and retrosellar extension; arterial encasement; brain invasion; coexisting cerebral aneurysms; and separate coexisting pathologies of the sphenoid sinus, especially infections. Residual/recurrent tumors and postoperative pituitary apoplexy after trans-sphenoidal surgery require individualized considerations. Transcranial approaches still have a critical role in giant and complex pituitary adenomas with wide intracranial extension, brain parenchymal involvement, and the encasement of neurovascular structures.

Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas

OBJECTIVES

Pituitary adenomas can exhibit aggressive behavior, characterized by rapid growth, resistance to conventional treatment, and early recurrence. This study aims to evaluate the clinical value of shape-related features combined with textural features based on conventional MRI in evaluating the aggressiveness of pituitary adenomas and develop the best diagnostic model.

METHODS

Two hundred forty-six pituitary adenoma patients (84 aggressive, 162 non-aggressive) who underwent preoperative MRI were retrospectively reviewed. The patients were divided into training (n = 193) and testing (n = 53) sets. Clinical information, shape-related, and textural features extracted from the tumor volume on contrast-enhanced T1-weighted images (CE-T1WI), were compared between aggressive and non-aggressive groups. Variables with significant differences were enrolled into Pearson's correlation analysis to weaken multicollinearity. Logistic regression models based on the selected features were constructed to predict tumor aggressiveness under fivefold cross-validation.

RESULTS

Sixty-five imaging features, including five shape-related and sixty textural features, were extracted from volumetric CE-T1WI. Forty-seven features were significantly different between aggressive and non-aggressive groups (all p values < 0.05). After feature selection, four features (SHAPE_Sphericity, SHAPE_Compacity, DISCRETIZED_Q3, and DISCRETIZED_Kurtosis) were put into logistic regression analysis. Based on the combination of these features and Knosp grade, the model yielded an area under the curve value of 0.935, with a sensitivity of 94.4% and a specificity of 82.9%, to discriminate between aggressive and non-aggressive pituitary adenomas in the testing set.

CONCLUSION

The radiomic model based on tumor shape and textural features study from CE-T1WI might potentially assist in the preoperative aggressiveness diagnosis of pituitary adenomas.

KEY POINTS

• Pituitary adenomas with aggressive behavior exhibit rapid growth, resistance to conventional treatment, and early recurrence despite gross resection and may require multiline treatments. • Shape-related features and texture features based on CE-T1WI were significantly correlated with the Ki-67 labeling index, mitotic count, and p53 expression, and the proposed model achieved a favorable prediction of the aggressiveness of PAs with an AUC value of 0.935. • The prediction model might provide valuable guidance for individualized treatment in patients with PAs.

Overview of the 2022 WHO Classification of Pituitary Adenomas/Pituitary Neuroendocrine Tumors: Clinical Practices, Controversies, and Perspectives

The latest edition of the WHO classification of the central nervous system was published in 2021. This review summarizes the major revisions to the classification of anterior pituitary tumors. The most important revision involves preferring the terminology of pituitary neuroendocrine tumor (PitNET), even though the terminology of pituitary adenoma (PA) still can be used according to this WHO classification compared to the previous one. Moreover, immunohistochemistry (IHC) examination of pituitary-specific transcription factors (TFs), including PIT1, TPIT, SF-1, GATA2/3, and ERα, is endorsed to determine the tumor cell lineage and to facilitate the classification of PitNET/PA subgroups. However, TF-negative IHC staining indicates PitNET/PA with no distinct cell lineages, which includes unclassified plurihormonal (PH) tumors and null cell (NC) tumors in this edition. The new WHO classification of PitNET/PA has incorporated tremendous advances in the understanding of the cytogenesis and pathogenesis of pituitary tumors. However, due to the shortcomings of the technology used in the diagnosis of PitNET/PA and the limited understanding of the tumorigenesis of PitNET/PA, the application of this new classification system in practice should be further evaluated and validated. Besides providing information for deciding the follow-up plans and adjunctive treatment after surgery, this classification system offers no additional help for neurosurgeons in clinical practice, especially in determining the treatment strategies. Therefore, it is necessary for neurosurgeons to establish a comprehensive pituitary classification system for PitNET/PA that incorporates neuroimaging grading data or direct observation of invasiveness during operation or the predictor of prognosis, as well as pathological diagnosis, thereby distinguishing the invasiveness of the tumor and facilitating neurosurgeons to decide on the treatment strategies and follow-up plans as well as adjunctive treatment after surgery.

Relationship with the diaphragm to predict the surgical outcome in large and giant pituitary adenomas

Objective

Large and giant pituitary adenomas (L- and G-PAs) continue to remain a surgical challenge. The diaphragm may have a role in determining the shape of the tumor and therefore influencing the extent of resection. Our study aims to analyze our surgical series of L- and G-PAs according to their relationship with the diaphragm and invasion of cavernous sinus (CS).

Material and methods

We performed a retrospective analysis of our surgical series of patients operated for L- and G-PAs. We categorized the tumors into four grades according to their relationship with the diaphragm: grade 1 (supradiaphragmatic component with a wide incompetent diaphragm), grade 2 (purely infra-diaphragmatic tumor with a competent diaphragm), grade 3 (dumbbell-shape tumors), and grade 4 (multilobulated tumor with invasion of the subarachnoid space).

Results

A total of 37 patients were included in our analysis. According to our classification, 43.3% of patients had grade 1 tumors, 27% had grade 2, 5.4% had grade 3, and 24.3% had grade 4 tumors. CS invasion was confirmed intraoperatively in 17 out of 37 patients (46%). The gross total resection (GTR) was obtained in 19% of the cases, near-total resection in 46%, and subtotal resection in 35%. All the patients who achieved GTR had grade 1 tumors and the lowest rate of CS invasion (p < 0.01).

Conclusion

Radiological evaluation of the tumor relationship with the diaphragm, invasion of CS, and invasion of the subarachnoid space are crucial to plan the surgical strategy and maximize the possibilities of achieving GTR in L- and G-PAs.

Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material

Soft material can undergo non-uniform deformation or change of shape upon processing. Identifying shape and its change is nevertheless not straightforward. In this study, novel image-based algorithm that can be used to identify shapes of input images and at the same time classify non-uniform deformation into various patterns, i.e., swelling/shrinkage, horizontal and vertical elongations/contractions as well as convexity and concavity, is proposed. The algorithm was first tested with computer-generated images and later applied to agar cubes, which were used as model shrinkable soft material, undergoing drying at different temperatures. Shape parameters and shape-parameter based algorithm as well as convolutional neural networks (CNNs) either incorrectly identified some complicated shapes or could only identify the point where non-uniform deformation started to take place; CNNs lacked ability to describe non-uniform deformation evolution. Shape identification accuracy of the newly developed algorithm against computer-generated images was 65.88%, while those of the other tested algorithms ranged from 34.76 to 97.88%. However, when being applied to the deformation of agar cubes, the developed algorithm performed superiorly to the others. The proposed algorithm could both identify the shapes and describe their changes. The interpretation agreed well with that via visual observation.