Preoperatively Predicting Ki67 Expression in Pituitary Adenomas Using Deep Segmentation Network and Radiomics Analysis Based on Multiparameter MRI

Preoperative Assessment of Ki-67 Labeling Index in Pituitary Adenomas Using Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI

BACKGROUND

Ki-67 labeling index (Ki-67 LI) is a proliferation marker that is correlated with aggressive behavior and prognosis of pituitary adenomas (PAs). Dynamic contrast-enhanced MRI (DCE-MRI) may potentially contribute to the preoperative assessment of Ki-67 LI.

PURPOSE

To investigate the feasibility of assessing Ki-67 LI of PAs preoperatively using delta-radiomics based on DCE-MRI.

STUDY TYPE

Retrospective.

POPULATION

605 PA patients (female = 47.1%, average age = 52.2) from two centers (high Ki-67 LI (≥ 3%) = 229; low Ki-67 LI (< 3%) = 376), divided into a training set (n = 313), an internal validation set (n = 196), and an external validation set (n = 96).

FIELD STRENGTH/SEQUENCE

1.5-T and 3-T, DCE-MRI.

ASSESSMENT

This study developed a non-delta-radiomics model based on the non-delta-radiomic features directly extracted from four phases, a delta-radiomics model based on the delta-radiomic features, and a combined model integrating clinical parameters (Knosp grade and tumor diameter) with delta-radiomic features. U test, recursive feature elimination (RFE), and least absolute shrinkage and selection operator (LASSO) regression were utilized to select important radiomic features. Support vector machine (SVM), XGBoost (XGB), logistic regression (LR), and Gaussian naive Bayes (GNB) were utilized to develop the models.

STATISTICAL TESTS

Receiver operating characteristic (ROC) curve. Calibration curve. Decision curve analysis (DCA). Intraclass correlation coefficients (ICC). DeLong test for ROC curves. U test or t test for numerical variables. Fisher's test or Chi-squared test for categorical variables. A p-value < 0.05 was considered statistically significant.

RESULTS

The combined model demonstrated the best performance in preoperatively assessing the Ki-67 LI of PAs, achieving AUCs of 0.937 and 0.897 in the internal and external validation sets, respectively. The models based on delta-radiomic features outperformed the non-delta-radiomic model.

DATA CONCLUSION

A delta-radiomics-based model using DCE-MRI may show high diagnostic performance for preoperatively assessing the Ki-67 LI status of PAs.

EVIDENCE LEVEL

3 Technical Efficacy: Stage 2.

Comparative analysis of the effects of microscopic vs. neuroendoscopic transsphenoidal surgery on visual and pituitary function and postoperative recurrence factors in patients with pituitary tumors

OBJECTIVE

To compare the effects of microscopic and neuroendoscopic transsphenoidal surgeries on visual function, pituitary function, and factors influencing postoperative recurrence in patients with pituitary tumors.

METHODS

A retrospective analysis was conducted on 164 patients with pituitary tumors who underwent surgery at The First People's Hospital of Xianyang from March 2020 to March 2022. Based on the surgical approach, patients were divided into an observation group (n=93) and a control group (n=71). The observation group underwent neuroendoscopic transsphenoidal pituitary tumor resection, while the control group underwent microscopic transsphenoidal pituitary tumor resection. General clinical data, perioperative indicators, hormone levels, quality of life, and olfactory function were compared between the two groups. Postoperative recurrence was recorded, and logistic regression analysis was performed to identify factors influencing postoperative recurrence in the patients.

RESULTS

The control group exhibited a greater amount of intraoperative bleeding and a longer postoperative hospital stay compared to the observation group (P<0.0001). The total tumor resection rate was significantly lower in the control group than that in the observation group (P=0.002). Additionally, the numbers of patients in the control group who experienced improvements in vision (P=0.013), headache (P=0.004), and sexual dysfunction (P=0.047) were lower than those in the observation group. One month after surgery, levels of prolactin, human growth hormone, adrenocorticotropic hormone, and thyroid-stimulating hormone were higher in the observation group than those in the control group. The quality of life score one month after surgery was higher in the observation group than that in the control group (P<0.0001). In addition, the olfactory function score one month after surgery was lower in the observation group compared to the control group (P<0.0001). The overall incidence of postoperative complications was higher in the control group (P=0.034). There was no statistically significant difference in recurrence rates between the two groups (P=0.102). Multivariate logistic regression analysis identified tumor size (P=0.001, OR=7.227), Knosp classification (P=0.005, OR=0.238), and Ki-67 index (P=0.001, OR=4.969) as independent risk factors for recurrence within two years in patients with pituitary tumors.

CONCLUSION

For patients with pituitary tumors, neuroendoscopic transsphenoidal surgery is more effective than microscopic transsphenoidal surgery in reducing operative time and improving postoperative visual and pituitary function, and therefore, should be promoted in clinical practice.

Preoperatively Predicting PIT1 Expression in Pituitary Adenomas Using Habitat, Intra-tumoral and Peri-tumoral Radiomics Based on MRI

The study aimed to predict expression of pituitary transcription factor 1 (PIT1) in pituitary adenomas using habitat, intra-tumoral and peri-tumoral radiomics models. A total of 129 patients with pituitary adenoma (training set, n = 103; test set, n = 26) were retrospectively enrolled. A total of 12, 18, 14, 13, and 14 radiomics features were selected from the ROIintra, ROIintra+peri (ROIintra+2mm, ROIintra+4mm, ROIintra+6mm), and ROIhabitat, respectively. Then, three machine learning algorithms were employed to develop radiomic models, including logistic regression (LR), support vector machines (SVM), and multilayer perceptron (MLP). The performances of the intra-tumoral, combined intra-tumoral and peri-tumoral, and habitat models were evaluated. The peritumoral region (ROI2mm, ROI4mm, ROI6mm) of the combined model with the highest performance was individually selected for further peritumoral analysis. Moreover, a deep learning radiomics nomogram (DLRN) was constructed incorporating clinical characteristics and the peri-tumoral and habitat models for individual prediction. The combined modelintra+2mm based on ROIintra+2mm achieved a better performance (AUC, 0.800) than that of the intra-tumoral model alone (AUC, 0.731). And the habitat model showed a higher performance (AUC, 0.806) than that of the intra-tumoral model. In addition, the performance of the peri-tumoral model based on ROI2mm was 0.694 in the testing set. Furthermore, the DLRN achieved the highest performance of 0.900 in the test set. The DLRN showed the best performance for PIT1 expression in pituitary adenomas, followed by the habitat, combined modelintra+2mm, intra-tumoral model, and peri-tumoral model based on ROI2mm, respectively. These different models are helpful for the model choice in clinical work.

Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models

OBJECTIVES

To preoperatively predict the high expression of Ki67 and positive pituitary transcription factor 1 (PIT-1) simultaneously in pituitary adenoma (PA) using three different radiomics models.

METHODS

A total of 247 patients with PA (training set: n = 198; test set: n = 49) were included in this retrospective study. The imaging features were extracted from preoperative contrast-enhanced T1WI (T1CE), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI). Feature selection was performed using Spearman's rank correlation coefficient and least absolute shrinkage and selection operator (LASSO). The classic machine learning (CML), deep learning (DL), and deep learning radiomics (DLR) models were constructed using logistic regression (LR), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and test sets. In addition, combined with clinical characteristics, the best CML and the best DL models (SVM classifier), the DL radiomics nomogram (DLRN) was constructed to aid clinical decision-making.

RESULTS

Seven CML features, 96 DL features, and 107 DLR features were selected to construct CML, DL and DLR models. Compared to CML and DL model, the DLR model had the best performance. The AUC, sensitivity, specificity, accuracy, NPV and PPV were 0.827, 0.792, 0.800, 0.796, 0.800 and 0.792 in the test set, respectively.

CONCLUSIONS

Compared with CML and DL models, the DLR model shows the best performance in predicting the Ki67 and PIT-1 expression in PAs simultaneously.

Automatic pituitary adenoma segmentation and identification of cavernous sinus invasion via multitask learning

AIM

This study aimed to develop a multitask deep learning model for pituitary macroadenoma (PMA) segmentation and identification of cavernous sinus (CS) invasion.

MATERIALS AND METHODS

A total of 926 patients with PMAs (n=816 from Institution 1 for model training and n=110 from Institution 2 for model validation) were included. Manual segmentation for tumor and bilateral internal carotid arteries was conducted on coronal contrast-enhanced T1-weighted imaging images using ITK-SNAP. Two performing neurosurgeons evaluated the CS invasion during the operation. A Multi-Task Multiaxis-Attention UNet (MTMAU-Net) framework that combined segmentation and CS invasion classification tasks was proposed. Dice similarity coefficient and 95% Hausdorff distance (HD95) were used to evaluate the segmentation performance. Accuracy and area under the curve (AUC) were used to assess the classification performance.

RESULTS

Compared with several single-task models, MTMAU-Net model achieved higher Dice coefficient (90.84±0.55%) and lower HD95 values (2.13±0.37 mm) in segmentation tasks and performed better in the classification task (accuracy: 88.05±2.84%, AUC: 0.89±0.08). Compared with the Knosp grading system, this model showed overall consistent performance (accuracy: 88.05% vs 87.68%) and outperformed human grading evaluation at Knosp grade 3 (83.33% vs 57.62%). MTMAU-Net showed great capability of generalization in both segmentation (Dice coefficient: 83.71±5.93%) and classification tasks (accuracy: 84.55%, AUC: 0.87) in the validation cohort.

CONCLUSION

MTMAU-Net outperformed single-task models in PMA segmentation and CS invasion classification and showed advantages over the Knosp grading system in identifying CS invasion, which might guide clinicians to make appropriate surgical strategies.

CLINICAL TRIAL REGISTRATION

This study is registered at the Chinese Clinical Trial Registration Center with registration number ChiCTR210047614.

Radiomics of pituitary adenoma using computer vision: a review

Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of "Radiomics" involves the extraction of high-dimensional features, often referred to as "Radiomic features," from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data

BACKGROUND

Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression.

METHODS

This study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression.

RESULTS

In total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient's MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031.

CONCLUSION

This study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma

Objectives

To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL).

Materials and methods

MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction.

Results

The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision.

Conclusion

In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task.

Clinical relevance statement

Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion

The aim of this study was to use multimodal imaging (contrast-enhanced T1-weighted (T1C), T2-weighted (T2), and diffusion-weighted imaging (DWI)) to develop a radiomics model for preoperatively predicting venous sinus invasion in meningiomas. This prediction would assist in selecting the appropriate surgical approach and forecasting the prognosis of meningiomas. A retrospective analysis was conducted on 331 participants who had been pathologically diagnosed with meningiomas. For each participant, 3948 radiomics features were acquired from the T1C, T2, and DWI images. Minimum redundancy maximum correlation, rank sum test, and multi-factor recursive elimination were used to extract the most significant features of different models. Then, multivariate logistic regression was used to build classification models to predict meningioma venous sinus invasion. The diagnostic capabilities were assessed using receiver operating characteristic (ROC) analysis. In addition, a nomogram was constructed by incorporating clinical and radiological characteristics and a radiomics signature. To assess the clinical usefulness of the nomogram, a decision curve analysis (DCA) was performed. Tumor shape, boundary, and enhancement features were independent predictors of meningioma venous sinus invasion (p = 0.013, p = 0.013, p = 0.005, respectively). Eleven (T2:1, T1C:4, DWI:6) of the 3948 radiomics features were screened for strong association with meningioma sinus invasion. The areas under the ROC curves for the training and external test sets were 0.946 and 0.874, respectively. The clinicoradiomic model showed excellent predictive performance for invasive meningioma, which may help to guide surgical approaches and predict prognosis.

Immunological signatures and predictive biomarkers for first-generation somatostatin receptor ligand resistance in Acromegaly

PURPOSE

Predicting resistance to first-generation Somatostatin Receptor Ligands (fg-SRL) in Acromegaly patients remains an ongong challenge. Tumor-associated immune components participate in various pathological processes, including drug-resistance. We aimed to identify the immune components involved in resistance of fg-SRL, and to investigate biomarkers that can be targeted to treat those drug-resistant Acromegaly.

METHODS

We conducted a retrospective study involving 35 Acromegaly patients with somatotropinomas treated postoperatively with fg-SRL. Gathering clinicopathological data, SSTR2 expression, and immunological profiles, we utilized univariate, binary logistic regression, and ROC analyses to assess their predictive roles in fg-SRL resistance. Spearman correlation analysis further examined interactions among interested characteristics.

RESULTS

19 patients (54.29%) exhibited resistance to postoperative fg-SRL. GH level at diagnosis, preoperative tumor volume, T2WI-MRI intensity, granularity, PD-L1, SSTR2, and CD8 + T cell infiltration showed association with clinical outcomes of fg-SRL. Notably, T2WI-MRI hyperintensity, PD-L1-IRS > 7, CD8 + T cell infiltration < 14.8/HPF, and SSTR2-IRS < 5.4 emerged as reliable predictors for fg-SRL resistance. Correlation analysis highlighted a negative relationship between PD-L1 expression and CD8 + T cell infiltration, while showcasing a positive correlation with preoperative tumor volume of somatotropinomas. Additionally, 5 patients with fg-SRL resistance underwent re-operation were involved. Following fg-SRL treatment, significant increases in PD-L1 and SSTR5 expression were observed, while SSTR2 expression decreased in somatotropinoma.

CONCLUSION

PD-L1 expression and CD8 + T cell infiltration, either independently or combined with SSTR2 expression and T2WI-MRI intensity, could form a predictive model guiding clinical decisions on fg-SRL employment. Furthermore, targeting PD-L1 through immunotherapy and embracing second-generations of SRL with higher affinity to SSTR5 represent promising strategies to tackle fg-SRL resistance in somatotropinomas.

Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives

Radiomic analysis has emerged as a valuable tool for extracting quantitative features from medical imaging data, providing in-depth insights into various contexts and diseases. By employing methods derived from advanced computational techniques, radiomics quantifies textural information through the evaluation of the spatial distribution of signal intensities and inter-voxel relationships. In recent years, these techniques have gained considerable attention also in the field of pituitary tumors, with promising results. Indeed, the extraction of radiomic features from pituitary magnetic resonance imaging (MRI) images has been shown to provide useful information on various relevant aspects of these diseases. Some of the key topics that have been explored in the existing literature include the association of radiomic parameters with histopathological and clinical data and their correlation with tumor invasiveness and aggressive behavior. Their prognostic value has also been evaluated, assessing their role in the prediction of post-surgical recurrence, response to medical treatments, and long-term outcomes. This review provides a comprehensive overview of the current knowledge and application of radiomics in pituitary tumors. It also examines the current limitations and future directions of radiomic analysis, highlighting the major challenges that need to be addressed before a consistent integration of these techniques into routine clinical practice.