Brain metastasis detection using machine learning: a systematic review and meta-analysis

Driving Knowledge to Action: Building a Better Future With Artificial Intelligence-Enabled Multidisciplinary Oncology

Artificial intelligence (AI) is transforming multidisciplinary oncology at an unprecedented pace, redefining how clinicians detect, classify, and treat cancer. From earlier and more accurate diagnoses to personalized treatment planning, AI's impact is evident across radiology, pathology, radiation oncology, and medical oncology. By leveraging vast and diverse data-including imaging, genomic, clinical, and real-world evidence-AI algorithms can uncover complex patterns, accelerate drug discovery, and help identify optimal treatment regimens for each patient. However, realizing the full potential of AI also necessitates addressing concerns regarding data quality, algorithmic bias, explainability, privacy, and regulatory oversight-especially in low- and middle-income countries (LMICs), where disparities in cancer care are particularly pronounced. This study provides a comprehensive overview of how AI is reshaping cancer care, reviews its benefits and challenges, and outlines ethical and policy implications in line with ASCO's 2025 theme, Driving Knowledge to Action. We offer concrete calls to action for clinicians, researchers, industry stakeholders, and policymakers to ensure that AI-driven, patient-centric oncology is accessible, equitable, and sustainable worldwide.

Exploring the Role of Peritumoral Edema in Predicting Lung Cancer Subtypes Through T2-FLAIR Digital Subtraction Imaging of Metastatic Brain Tumors

Background/Objectives: This study aimed to investigate whether small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) can be distinguished based on radiomics data derived from T2-FLAIR digital subtraction images of the peritumoral edema region in patients with brain metastases. Methods: A total of 136 patients who underwent surgery for brain tumors, including 100 patients in the Pretreat-Metstobrain-MASKS dataset and 36 patients from our institution, were included in our study. Radiomic features were extracted from digitally subtracted T2-FLAIR images in the peritumoral edema area. Patients were divided into NSCLC and SCLC groups. The maximum relevance-minimum redundancy (mRMR) method was then used for dimensionality reduction. The Naive Bayes algorithm was used for model development, and the interpretability of the model was explored using SHapley Additive exPlanations (SHAP). The performance metrics included the area under the curve (AUC), sensitivity (SENS), and specificity (SPEC). Results: The mean age of NSCLC patients was 64.6 ± 10.3 years, and that of SCLC patients was 63.4 ± 11.7 years. In the external validation cohort, the model achieved an AUC of 0.82 (0.68-0.97), a SENS of 0.87 (0.74-0.91), and a SPEC of 0.72 (0.72-0.89). In the train cohort, the model achieved an AUC of 1.000, a SENS of 1.000, and a SPEC of 1.000. The feature providing the best effect was wavelet-HHHglcmJointEnergy, with a SHAP value of approximately 2.5. Conclusions: An artificial intelligence model developed using radiomics data from T2-FLAIR digital subtraction images of the peritumoral edema area can identify the histologic type of lung cancer in patients with associated brain metastases.

Radiomics and Artificial Intelligence Landscape for [18F]FDG PET/CT in Multiple Myeloma

[18F]FDG PET/CT is a powerful imaging modality of high performance in multiple myeloma (MM) and is considered the appropriate method for assessing treatment response in this disease. On the other hand, due to the heterogeneous and sometimes complex patterns of bone marrow infiltration in MM, the interpretation of PET/CT can be particularly challenging, hampering interobserver reproducibility and limiting the diagnostic and prognostic ability of the modality. Although many approaches have been developed to address the issue of standardization, none can yet be considered a standard method for interpretation or objective quantification of PET/CT. Therefore, advanced diagnostic quantification approaches are needed to support and potentially guide the management of MM. In recent years, radiomics has emerged as an innovative method for high-throughput mining of image-derived features for clinical decision making, which may be particularly helpful in oncology. In addition, machine learning and deep learning, both subfields of artificial intelligence (AI) closely related to the radiomics process, have been increasingly applied to automated image analysis, offering new possibilities for a standardized evaluation of imaging modalities such as CT, PET/CT and MRI in oncology. In line with this, the initial but steadily growing literature on the application of radiomics and AI-based methods in the field of [18F]FDG PET/CT in MM has already yielded encouraging results, offering a potentially reliable tool towards optimization and standardization of interpretation in this disease. The main results of these studies are presented in this review.

Machine learning-based reconstruction of prognostic staging for gastric cancer patients with different differentiation grades: A multicenter retrospective study

BACKGROUND

The prognosis of gastric cancer (GC) patients is poor, and an accurate prognostic staging system would help assess patients' prognostic status before treatment and determine appropriate treatment strategies.

AIM

To develop positive lymph node ratio (LNR) and machine learning (ML)-based staging systems for GC patients with varying differentiation.

METHODS

This multicenter retrospective cohort study included 11772 GC patients, with 5612 in the training set (Harbin Medical University Cancer Hospital) and 6160 in the validation set (Surveillance, Epidemiology, and End Results Program database). X-tile software identified optimal cutoff values for the positive LNR, and five ML models were developed using pT and LNR staging. Risk scores were divided into seven stages, constructing new staging systems tailored to different tumor differentiation levels.

RESULTS

In both the training and validation sets, regardless of the tumor differentiation level, LNR staging demonstrated superior prognostic stratification compared to pN. Extreme Gradient Boosting exhibited better predictive performance than the other four models. Compared to tumor node metastasis staging, the new staging systems, developed for patients with different degrees of differentiation, showed significantly better predictive performance.

CONCLUSION

The new positive lymph nodes ratio staging and integrated staging systems constructed for GC patients with different differentiation grades exhibited better prognostic stratification capabilities.

A 3D Convolutional Neural Network Based on Non-enhanced Brain CT to Identify Patients with Brain Metastases

Dedicated brain imaging for cancer patients is seldom recommended in the absence of symptoms. There is increasing availability of non-enhanced CT (NE-CT) of the brain, mainly owing to a wider utilization of Positron Emission Tomography-CT (PET-CT) in cancer staging. Brain metastases (BM) are often hard to diagnose on NE-CT. This work aims to develop a 3D Convolutional Neural Network (3D-CNN) based on brain NE-CT to distinguish patients with and without BM. We retrospectively included NE-CT scans for 100 patients with single or multiple BM and 100 patients without brain imaging abnormalities. Patients whose largest lesion was < 5 mm were excluded. The largest tumor was manually segmented on a matched contrast-enhanced T1 weighted Magnetic Resonance Imaging (MRI), and shape radiomics were extracted to determine the size and volume of the lesion. The brain was automatically segmented, and masked images were normalized and resampled. The dataset was split into training (70%) and validation (30%) sets. Multiple versions of a 3D-CNN were developed, and the best model was selected based on accuracy (ACC) on the validation set. The median largest tumor Maximum-3D-Diameter was 2.29 cm, and its median volume was 2.81 cc. Solitary BM were found in 27% of the patients, while 49% had > 5 BMs. The best model consisted of 4 convolutional layers with 3D average pooling layers, dropout layers of 50%, and a sigmoid activation function. Mean validation ACC was 0.983 (SD: 0.020) and mean area under receiver-operating characteristic curve was 0.983 (SD: 0.023). Sensitivity was 0.983 (SD: 0.020). We developed an accurate 3D-CNN based on brain NE-CT to differentiate between patients with and without BM. The model merits further external validation.

Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up

RATIONALE AND OBJECTIVES

The RANO-BM criteria, which employ a one-dimensional measurement of the largest diameter, are imperfect due to the fact that the lesion volume is neither isotropic nor homogeneous. Furthermore, this approach is inherently time-consuming. Consequently, in clinical practice, monitoring patients in clinical trials in compliance with the RANO-BM criteria is rarely achieved. The objective of this study was to develop and validate an AI solution capable of delineating brain metastases (BM) on MRI to easily obtain, using an in-house solution, RANO-BM criteria as well as BM volume in a routine clinical setting.

MATERIALS (PATIENTS) AND METHODS

A total of 27,456 post-Gadolinium-T1 MRI from 132 patients with BM were employed in this study. A deep learning (DL) model was constructed using the PyTorch and PyTorch Lightning frameworks, and the UNETR transfer learning method was employed to segment BM from MRI.

RESULTS

A visual analysis of the AI model results demonstrates confident delineation of the BM lesions. The model shows 100 % accuracy in predicting RANO-BM criteria in comparison to that of an expert medical doctor. There was a high degree of overlap between the AI and the doctor's segmentation, with a mean DICE score of 0.77. The diameter and volume of the BM lesions were found to be concordant between the AI and the reference segmentation. The user interface developed in this study can readily provide RANO-BM criteria following AI BM segmentation.

CONCLUSION

The in-house deep learning solution is accessible to everyone without expertise in AI and offers effective BM segmentation and substantial time savings.

Metastatic brain tumors: from development to cutting-edge treatment

Metastatic brain tumors, also called brain metastasis (BM), represent a challenging complication of advanced tumors. Tumors that commonly metastasize to the brain include lung cancer and breast cancer. In recent years, the prognosis for BM patients has improved, and significant advancements have been made in both clinical and preclinical research. This review focuses on BM originating from lung cancer and breast cancer. We briefly overview the history and epidemiology of BM, as well as the current diagnostic and treatment paradigms. Additionally, we summarize multiomics evidence on the mechanisms of tumor occurrence and development in the era of artificial intelligence and discuss the role of the tumor microenvironment. Preclinically, we introduce the establishment of BM models, detailed molecular mechanisms, and cutting-edge treatment methods. BM is primarily treated with a comprehensive approach, including local treatments such as surgery and radiotherapy. For lung cancer, targeted therapy and immunotherapy have shown efficacy, while in breast cancer, monoclonal antibodies, tyrosine kinase inhibitors, and antibody-drug conjugates are effective in BM. Multiomics approaches assist in clinical diagnosis and treatment, revealing the complex mechanisms of BM. Moreover, preclinical agents often need to cross the blood-brain barrier to achieve high intracranial concentrations, including small-molecule inhibitors, nanoparticles, and peptide drugs. Addressing BM is imperative.

The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

Hippocampal protection during preventive cranial irradiation and neurocognitive functions in patients with small cell lung cancer

Background

In small cell lung cancer (SCLC), limiting the radiation dose in the hippocampus area during preventive cranial irradiation (PCI) can reduce nerve injury and cognitive decline. This study was done to compare changes in cognitive functions between hippocampal-protected (3D-H) and non-hippocampal-protected (3D) patients during PCI.

Materials and methods

the study group included 113 patients with SCLC qualified to PCI divided in two subgroups: 3D-H (n = 74) and 3D (n = 39). Two diagnostic and screening tests, Mini-Mental State Examination (MMSE) Short Scale and Montreal Cognitive Assessment (MoCA) Scale, have been applied before the start of irradiation, immediately after and 3 months after PCI.

Results

The doses delivered to the volume of the left and right hippocampus were similar and amounted to 12.00 Gy and 12.05 Gy, respectively. There were no differences between 3D-H and 3D groups in the MoCA and MMSE tests at any time point. In both groups the values in MoCA and MMSE scales differed between time points I, II and III. The patients in the 3D-H group were less likely than patients in 3D group to experience significant cognitive decline on the MoCA scale (p = 0.003), but not on the MMSE scale (p = 0.103).

Conclusions

Following PCI, SCLC patients experience significant cognitive decline, even when the radiation dose in the hippocampal area is reduced. This trend continues for at least 3 months following the PCI. In hippocampal-protected patients significant cognitive decline assessed on the MoCA scale is less common than in non-hippocampal-protected patients.

A systematic review on artificial intelligence evaluating PSMA PET scan for intraprostatic cancer

OBJECTIVES

To assess artificial intelligence (AI) ability to evaluate intraprostatic prostate cancer (PCa) on prostate-specific membrane antigen positron emission tomography (PSMA PET) scans prior to active treatment (radiotherapy or prostatectomy).

MATERIALS AND METHODS

This systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD42023438706). A search was performed on Medline, Embase, Web of Science, and Engineering Village with the following terms: 'artificial intelligence', 'prostate cancer', and 'PSMA PET'. All articles published up to February 2024 were considered. Studies were included if patients underwent PSMA PET scan to evaluate intraprostatic lesions prior to active treatment. The two authors independently evaluated titles, abstracts, and full text. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used.

RESULTS

Our search yield 948 articles, of which 14 were eligible for inclusion. Eight studies met the primary endpoint of differentiating high-grade PCa. Differentiating between International Society of Urological Pathology (ISUP) Grade Group (GG) ≥3 PCa had an accuracy between 0.671 to 0.992, sensitivity of 0.91, specificity of 0.35. Differentiating ISUP GG ≥4 PCa had an accuracy between 0.83 and 0.88, sensitivity was 0.89, specificity was 0.87. AI could identify non-PSMA-avid lesions with an accuracy of 0.87, specificity of 0.85, and specificity of 0.89. Three studies demonstrated ability of AI to detect extraprostatic extensions with an area under curve between 0.70 and 0.77. Lastly, AI can automate segmentation of intraprostatic lesion and measurement of gross tumour volume.

CONCLUSION

Although the current state of AI differentiating high-grade PCa is promising, it remains experimental and not ready for routine clinical application. Benefits of using AI to assess intraprostatic lesions on PSMA PET scans include: local staging, identifying otherwise radiologically occult lesions, standardisation and expedite reporting of PSMA PET scans. Larger, prospective, multicentre studies are needed.

Deep learning ensembles for detecting brain metastases in longitudinal multi-modal MRI studies

Metastatic brain cancer is a condition characterized by the migration of cancer cells to the brain from extracranial sites. Notably, metastatic brain tumors surpass primary brain tumors in prevalence by a significant factor, they exhibit an aggressive growth potential and have the capacity to spread across diverse cerebral locations simultaneously. Magnetic resonance imaging (MRI) scans of individuals afflicted with metastatic brain tumors unveil a wide spectrum of characteristics. These lesions vary in size and quantity, spanning from tiny nodules to substantial masses captured within MRI. Patients may present with a limited number of lesions or an extensive burden of hundreds of them. Moreover, longitudinal studies may depict surgical resection cavities, as well as areas of necrosis or edema. Thus, the manual analysis of such MRI scans is difficult, user-dependent and cost-inefficient, and - importantly - it lacks reproducibility. We address these challenges and propose a pipeline for detecting and analyzing brain metastases in longitudinal studies, which benefits from an ensemble of various deep learning architectures originally designed for different downstream tasks (detection and segmentation). The experiments, performed over 275 multi-modal MRI scans of 87 patients acquired in 53 sites, coupled with rigorously validated manual annotations, revealed that our pipeline, built upon open-source tools to ensure its reproducibility, offers high-quality detection, and allows for precisely tracking the disease progression. To objectively quantify the generalizability of models, we introduce a new data stratification approach that accommodates the heterogeneity of the dataset and is used to elaborate training-test splits in a data-robust manner, alongside a new set of quality metrics to objectively assess algorithms. Our system provides a fully automatic and quantitative approach that may support physicians in a laborious process of disease progression tracking and evaluation of treatment efficacy.

A systematic review of deep learning-based spinal bone lesion detection in medical images

Spinal bone lesions encompass a wide array of pathologies, spanning from benign abnormalities to aggressive malignancies, such as diffusely localized metastases. Early detection and accurate differentiation of the underlying diseases is crucial for every patient's clinical treatment and outcome, with radiological imaging being a core element in the diagnostic pathway. Across numerous pathologies and imaging techniques, deep learning (DL) models are progressively considered a valuable resource in the clinical setting. This review describes not only the diagnostic performance of these models and the differing approaches in the field of spinal bone malignancy recognition, but also the lack of standardized methodology and reporting that we believe is currently hampering this newly founded area of research. In line with their established and reliable role in lesion detection, this publication focuses on both computed tomography and magnetic resonance imaging, as well as various derivative modalities (i.e. SPECT). After conducting a systematic literature search and subsequent analysis for applicability and quality using a modified QUADAS-2 scoring system, we confirmed that most of the 14 identified studies were plagued by major limitations, such as insufficient reporting of model statistics and data acquisition, a lacking external validation dataset, and potentially biased annotation. Although we experienced these limitations, we nonetheless conclude that the potential of these methods shines through in the presented results. These findings underline the need for more stringent quality controls in DL studies, as well as model development to afford increased insight and progress in this promising novel field.

Imaging Recommendations for Diagnosis, Staging, and Management of Central Nervous System Neoplasms in Adults: CNS Metastases

Brain metastases (BMs) are the most common central nervous system (CNS) neoplasms, with an increasing incidence that is due in part to an overall increase in primary cancers, improved neuroimaging modalities leading to increased detection, better systemic therapies, and longer patient survival.

OBJECTIVE

To identify cancer patients at a higher risk of developing CNS metastases and to evaluate associated prognostic factors.

METHODS

Review of imaging referral guidelines, response criteria, interval imaging assessment, modality of choice, as well as the association of clinical, serological, and imaging findings as per various cancer societies.

RESULTS

Quantitative response assessment of target and non-target brain metastases as well as an interval imaging protocol set up based on primary histological diagnosis and therapy status are discussed as per various cancer societies and imaging programs.

CONCLUSION

Predictive factors in the primary tumor as well as independent variables of brain metastases like size, number, and response to therapy are necessary in management. The location of CNS metastases, symptomatic disease, as well as follow up imaging findings form a skeletal plan to prognosticate the disease, keeping in mind all the available new advanced therapy options of surgery, radiation, and immunotherapy that improve patient outcome significantly.

Role of artificial intelligence in brain tumour imaging

Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In this review, we discuss how AI can assist in brain tumour imaging, focusing on machine learning (ML) and deep learning (DL) techniques. We describe how AI can help in lesion detection, differential diagnosis, anatomic segmentation, molecular marker identification, prognostication, and pseudo-progression evaluation. We also cover AI applications in non-glioma brain tumours, such as brain metastasis, posterior fossa, and pituitary tumours. We highlight the challenges and limitations of AI implementation in radiology, such as data quality, standardization, and integration. Based on the findings in the aforementioned areas, we conclude that AI can potentially improve the diagnosis and treatment of brain tumours and provide a path towards personalized medicine and better patient outcomes.

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.

Advances in determining the gross tumor target volume for radiotherapy of brain metastases

Brain metastases (BMs) are the most prevalent intracranial malignant tumors in adults and are the leading cause of mortality attributed to malignant brain diseases. Radiotherapy (RT) plays a critical role in the treatment of BMs, with local RT techniques such as stereotactic radiosurgery (SRS)/stereotactic body radiotherapy (SBRT) showing remarkable therapeutic effectiveness. The precise determination of gross tumor target volume (GTV) is crucial for ensuring the effectiveness of SRS/SBRT. Multimodal imaging techniques such as CT, MRI, and PET are extensively used for the diagnosis of BMs and GTV determination. With the development of functional imaging and artificial intelligence (AI) technology, there are more innovative ways to determine GTV for BMs, which significantly improve the accuracy and efficiency of the determination. This article provides an overview of the progress in GTV determination for RT in BMs.

Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences.

Reducing false positives in deep learning-based brain metastasis detection by using both gradient-echo and spin-echo contrast-enhanced MRI: validation in a multi-center diagnostic cohort

OBJECTIVES

To develop a deep learning (DL) for detection of brain metastasis (BM) that incorporates both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced DL) and evaluate it in a clinical cohort in comparison with human readers and DL using gradient-echo-based imaging only (GRE DL).

MATERIALS AND METHODS

DL detection was developed using data from 200 patients with BM (training set) and tested in 62 (internal) and 48 (external) consecutive patients who underwent stereotactic radiosurgery and diagnostic dual-enhanced imaging (dual-enhanced DL) and later guide GRE imaging (GRE DL). The detection sensitivity and positive predictive value (PPV) were compared between two DLs. Two neuroradiologists independently analyzed BM and reference standards for BM were separately drawn by another neuroradiologist. The relative differences (RDs) from the reference standard BM numbers were compared between the DLs and neuroradiologists.

RESULTS

Sensitivity was similar between GRE DL (93%, 95% confidence interval [CI]: 90-96%) and dual-enhanced DL (92% [89-94%]). The PPV of the dual-enhanced DL was higher (89% [86-92%], p < .001) than that of GRE DL (76%, [72-80%]). GRE DL significantly overestimated the number of metastases (false positives; RD: 0.05, 95% CI: 0.00-0.58) compared with neuroradiologists (RD: 0.00, 95% CI: - 0.28, 0.15, p < .001), whereas dual-enhanced DL (RD: 0.00, 95% CI: 0.00-0.15) did not show a statistically significant difference from neuroradiologists (RD: 0.00, 95% CI: - 0.20-0.10, p = .913).

CONCLUSION

The dual-enhanced DL showed improved detection of BM and reduced overestimation compared with GRE DL, achieving similar performance to neuroradiologists.

CLINICAL RELEVANCE STATEMENT

The use of deep learning-based brain metastasis detection with turbo spin-echo imaging reduces false positive detections, aiding in the guidance of stereotactic radiosurgery when gradient-echo imaging alone is employed.

KEY POINTS

•Deep learning for brain metastasis detection improved by using both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced deep learning). •Dual-enhanced deep learning increased true positive detections and reduced overestimation. •Dual-enhanced deep learning achieved similar performance to neuroradiologists for brain metastasis counts.

Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study

OBJECTIVES

To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting.

MATERIALS AND METHODS

In this retrospective study, a DLS was developed in 101 patients and validated on 264 consecutive patients (with lung cancer) having newly developed BM from two tertiary university hospitals, which performed black-blood imaging between January 2020 and April 2021. Four neuroradiologists independently evaluated BM either with segmented masks and BM counts provided (with DLS) or not provided (without DLS) on a clinical trial imaging management system (CTIMS). To assess reading reproducibility, BM count agreement between the readers and the reference standard were calculated using limits of agreement (LoA). Readers' workload was assessed with reading time, which was automatically measured on CTIMS, and were compared between with and without DLS using linear mixed models considering the imaging center.

RESULTS

In the validation cohort, the detection sensitivity and positive predictive value of the DLS were 90.2% (95% confidence interval [CI]: 88.1-92.2) and 88.2% (95% CI: 85.7-90.4), respectively. The difference between the readers and the reference counts was larger without DLS (LoA: -0.281, 95% CI: -2.888, 2.325) than with DLS (LoA: -0.163, 95% CI: -2.692, 2.367). The reading time was reduced from mean 66.9 s (interquartile range: 43.2-90.6) to 57.3 s (interquartile range: 33.6-81.0) (P <.001) in the with DLS group, regardless of the imaging center.

CONCLUSION

Deep learning-based BM detection and counting with black-blood imaging improved reproducibility and reduced reading time, on multi-center validation.

A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information

Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.

Predicting EGFR Mutation Status in Non-Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVES

Recent advancements in artificial intelligence (AI) render a substantial promise for epidermal growth factor receptor (EGFR) mutation status prediction in non-small cell lung cancer (NSCLC). We aimed to evaluate the performance and quality of AI algorithms that use radiomics features in predicting EGFR mutation status in patient with NSCLC.

MATERIALS AND METHODS

We searched PubMed (Medline), EMBASE, Web of Science, and IEEExplore for studies published up to February 28, 2022. Studies utilizing an AI algorithm (either conventional machine learning [cML] and deep learning [DL]) for predicting EGFR mutations in patients with NSLCL were included. We extracted binary diagnostic accuracy data and constructed a bivariate random-effects model to obtain pooled sensitivity, specificity, and 95% confidence interval. This study is registered with PROSPERO, CRD42021278738.

RESULTS

Our search identified 460 studies, of which 42 were included. Thirty-five studies were included in the meta-analysis. The AI algorithms exhibited an overall area under the curve (AUC) value of 0.789 and pooled sensitivity and specificity levels of 72.2% and 73.3%, respectively. The DL algorithms outperformed cML in terms of AUC (0.822 vs. 0.775) and sensitivity (80.1% vs. 71.1%), but had lower specificity (70.0% vs. 73.8%, p-value < 0.001) compared to cML. Subgroup analysis revealed that the use of positron-emission tomography/computed tomography, additional clinical information, deep feature extraction, and manual segmentation can improve diagnostic performance.

CONCLUSION

DL algorithms can serve as a novel method for increasing predictive accuracy and thus have considerable potential for use in predicting EGFR mutation status in patient with NSCLC. We also suggest that guidelines on using AI algorithms in medical image analysis should be developed with a focus on oncologic radiomics.

Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis

BACKGROUND

Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images.

METHODS

We conducted a comprehensive search of PubMed, Embase, and Web of Science up to May 24, 2023, which yielded 42 relevant studies for our analysis. We assessed the quality of these studies using the QUADAS-2 and CLAIM tools. Using a random-effect model, we calculated the pooled lesion-wise dice score as well as patient-wise and lesion-wise sensitivity. We performed subgroup analyses to investigate the influence of factors such as publication year, study design, training center of the model, validation methods, slice thickness, model input dimensions, MRI sequences fed to the model, and the specific deep learning algorithms employed. Additionally, meta-regression analyses were carried out considering the number of patients in the studies, count of MRI manufacturers, count of MRI models, training sample size, and lesion number.

RESULTS

Our analysis highlighted that deep learning models, particularly the U-Net and its variants, demonstrated superior segmentation accuracy. Enhanced detection sensitivity was observed with an increased diversity in MRI hardware, both in terms of manufacturer and model variety. Furthermore, slice thickness was identified as a significant factor influencing lesion-wise detection sensitivity. Overall, the pooled results indicated a lesion-wise dice score of 79%, with patient-wise and lesion-wise sensitivities at 86% and 87%, respectively.

CONCLUSIONS

The study underscores the potential of deep learning in improving brain metastasis diagnostics and treatment planning. Still, more extensive cohorts and larger meta-analysis are needed for more practical and generalizable algorithms. Future research should prioritize these areas to advance the field. This study was funded by the Gen. & Mrs. M.C. Peng Fellowship and registered under PROSPERO (CRD42023427776).

Systematic reviews of machine learning in healthcare: a literature review

INTRODUCTION

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

METHODS

A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted.

RESULTS

In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively).

EXPERT OPINION

The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.

Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery

The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in stereotactic radiosurgery. The rapid evolution of AI technology has led to promising developments in this field, particularly through the utilization of machine learning and deep learning models. The diverse implementation of AI algorithms was developed from various aspects of radiosurgery, including the successful detection of spontaneous tumors and the automated delineation or segmentation of lesions. These applications show potential for extension to longitudinal treatment follow-up. Additionally, the chapter highlights the established use of machine learning algorithms, particularly those incorporating radiomic-based analysis, in predicting treatment outcomes. The discussion encompasses current achievements, existing limitations, and the need for further investigation in the dynamic intersection of AI and radiosurgery.

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

Background

The early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients.

Methods

A systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables.

Results

This systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables.

Conclusion

Although the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.

Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer: A Diagnostic Test Accuracy Study

OBJECTIVES

Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times.

MATERIALS AND METHODS

A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated.

RESULTS

Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker ( P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML).

CONCLUSIONS

There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support.

Can machine learning models improve early detection of brain metastases using diffusion weighted imaging-based radiomics?

Background

Metastatic complications are a major cause of cancer-related morbidity, with up to 40% of cancer patients experiencing at least one brain metastasis. Earlier detection may significantly improve patient outcomes and overall survival. We investigated machine learning (ML) models for early detection of brain metastases based on diffusion weighted imaging (DWI) radiomics.

Methods

Longitudinal diffusion imaging from 116 patients previously treated with stereotactic radiosurgery (SRS) for brain metastases were retrospectively analyzed. Clinical contours from 600 metastases were extracted from radiosurgery planning computed tomography, and rigidly registered to corresponding contrast enhanced-T1 and apparent diffusion coefficient (ADC) maps. Contralateral contours located in healthy brain tissue were used as control. The dataset consisted of (I) radiomic features using ADC maps, (II) radiomic feature change calculated using timepoints before the metastasis manifested on contrast enhanced-T1, (III) primary cancer, and (IV) anatomical location. The dataset was divided into training and internal validation sets using an 80/20 split with stratification. Four classification algorithms [Linear Support Vector Machine (SVM), Random Forest (RF), AdaBoost, and XGBoost] underwent supervised classification training, with contours labeled either 'control' or 'metastasis'. Hyperparameters were optimized towards balanced accuracy. Various model metrics (receiver operating characteristic curve area scores, accuracy, recall, and precision) were calculated to gauge performance.

Results

The radiomic and clinical data set, feature engineering, and ML models developed were able to identify metastases with an accuracy of up to 87.7% on the training set, and 85.8% on an unseen test set. XGBoost and RF showed superior accuracy (XGBoost: 0.877±0.021 and 0.833±0.47, RF: 0.823±0.024 and 0.858±0.045) for training and validation sets, respectively. XGBoost and RF also showed strong area under the receiver operating characteristic curve (AUC) performance on the validation set (0.910±0.037 and 0.922±0.034, respectively). AdaBoost performed slightly lower in all metrics. SVM model generalized poorly with the internal validation set. Important features involved changes in radiomics months before manifesting on contrast enhanced-T1.

Conclusions

The proposed models using diffusion-based radiomics showed encouraging results in differentiating healthy brain tissue from metastases using clinical imaging data. These findings suggest that longitudinal diffusion imaging and ML may help improve patient care through earlier diagnosis and increased patient monitoring/follow-up. Future work aims to improve model classification metrics, robustness, user-interface, and clinical applicability.

Feature pyramid network-based computer-aided detection and monitoring treatment response of brain metastases on contrast-enhanced MRI

AIM

To investigate the value of feature pyramid network (FPN)-based computer-aided detection (CAD) of brain metastases (BMs) before and after non-surgical treatment, and to evaluate its performance in monitoring treatment response of BM on contrast-enhanced (CE) magnetic resonance imaging (MRI).

MATERIAL AND METHODS

Eighty-five cancer patients newly diagnosed with BM who had undergone initial and follow-up three-dimensional (3D) CE MRI at Liaocheng People's Hospital were included retrospectively in this study. Manual detection (MD) was performed by reviewer 1. Computer-aided detection (CAD) was performed by reviewer 2 using uAI Discover-BMs software. The treatment response was assessed by the two reviewers for each patient separately. A paired chi-square test was used to compare the differences in the detection of BM between MD and CAD. Agreement between MD and CAD in monitoring treatment response was assessed by kappa test.

RESULTS

The sensitivities of MD and CAD on initial 3D CE MRI were 78.65% and 99.13%, respectively. The sensitivities of MD and CAD on follow-up 3D CE MRI were 76.32% and 98.24%, respectively. There was a very good agreement between Reviewer 1 and Reviewer 2 in evaluating the treatment response of BM.

CONCLUSION

FPN-based CAD has a higher sensitivity of close to 100% and lower false negatives (FNs) for BM detection, compared to MD. Although CAD had a few shortcomings in reflecting changes of BMs after treatment, it had high performance in monitoring treatment response of BM on CE MRI.

Construction and evaluation of a gated high-resolution neural network for automatic brain metastasis detection and segmentation

OBJECTIVES

To construct and evaluate a gated high-resolution convolutional neural network for detecting and segmenting brain metastasis (BM).

METHODS

This retrospective study included craniocerebral MRI scans of 1392 patients with 14,542 BMs and 200 patients with no BM between January 2012 and April 2022. A primary dataset including 1000 cases with 11,686 BMs was employed to construct the model, while an independent dataset including 100 cases with 1069 BMs from other hospitals was used to examine the generalizability. The potential of the model for clinical use was also evaluated by comparing its performance in BM detection and segmentation to that of radiologists, and comparing radiologists' lesion detecting performances with and without model assistance.

RESULTS

Our model yielded a recall of 0.88, a dice similarity coefficient (DSC) of 0.90, a positive predictive value (PPV) of 0.93 and a false positives per patient (FP) of 1.01 in the test set, and a recall of 0.85, a DSC of 0.89, a PPV of 0.93, and a FP of 1.07 in dataset from other hospitals. With the model's assistance, the BM detection rates of 4 radiologists improved significantly, ranging from 5.2 to 15.1% (all p < 0.001), and also for detecting small BMs with diameter ≤ 5 mm (ranging from 7.2 to 27.0%, all p < 0.001).

CONCLUSIONS

The proposed model enables accurate BM detection and segmentation with higher sensitivity and less time consumption, showing the potential to augment radiologists' performance in detecting BM.

CLINICAL RELEVANCE STATEMENT

This study offers a promising computer-aided tool to assist the brain metastasis detection and segmentation in routine clinical practice for cancer patients.

KEY POINTS

• The GHR-CNN could accurately detect and segment BM on contrast-enhanced 3D-T1W images. • The GHR-CNN improved the BM detection rate of radiologists, including the detection of small lesions. • The GHR-CNN enabled automated segmentation of BM in a very short time.

Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma

PURPOSE

[18F]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients.

MATERIALS AND METHODS

Whole-body [18F]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [18F]FDG-avid lesions as well as the degree of diffuse [18F]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1-6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian × 1.1 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 2: liver SUVmedian × 1.5 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 3: liver SUVmedian × 2 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton), ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients.

RESULTS

BM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [18F]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of β2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5.

CONCLUSIONS

The automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts.

A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information

Resection and whole brain radiotherapy (WBRT) are the standards of care for the treatment of patients with brain metastases (BM) but are often associated with cognitive side effects. Stereotactic radiosurgery (SRS) involves a more targeted treatment approach and has been shown to avoid the side effects associated with WBRT. However, SRS requires precise identification and delineation of BM. While many AI algorithms have been developed for this purpose, their clinical adoption has been limited due to poor model performance in the clinical setting. Major reasons for non-generalizable algorithms are the limitations in the datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models to improve generalizability. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and whole tumor (including peritumoral edema) 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging feature information. We used a streamlined approach to database-building leveraging a PACS-integrated segmentation workflow.

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.

Predicting leptomeningeal disease spread after resection of brain metastases using machine learning

OBJECTIVE

The incidence of leptomeningeal disease (LMD) has increased as treatments for brain metastases (BMs) have improved and patients with metastatic disease are living longer. Sample sizes of individual studies investigating LMD after surgery for BMs and its risk factors have been limited, ranging from 200 to 400 patients at risk for LMD, which only allows the use of conventional biostatistics. Here, the authors used machine learning techniques to enhance LMD prediction in a cohort of surgically treated BMs.

METHODS

A conditional survival forest, a Cox proportional hazards model, an extreme gradient boosting (XGBoost) classifier, an extra trees classifier, and logistic regression were trained. A synthetic minority oversampling technique (SMOTE) was used to train the models and handle the inherent class imbalance. Patients were divided into an 80:20 training and test set. Fivefold cross-validation was used on the training set for hyperparameter optimization. Patients eligible for study inclusion were adults who had consecutively undergone neurosurgical BM treatment, had been admitted to Brigham and Women's Hospital from January 2007 through December 2019, and had a minimum of 1 month of follow-up after neurosurgical treatment.

RESULTS

A total of 1054 surgically treated BM patients were included in this analysis. LMD occurred in 168 patients (15.9%) at a median of 7.05 months after BM diagnosis. The discrimination of LMD occurrence was optimal using an XGboost algorithm (area under the curve = 0.83), and the time to LMD was prognosticated evenly by the random forest algorithm and the Cox proportional hazards model (C-index = 0.76). The most important feature for both LMD classification and regression was the BM proximity to the CSF space, followed by a cerebellar BM location. Lymph node metastasis of the primary tumor at BM diagnosis and a cerebellar BM location were the strongest risk factors for both LMD occurrence and time to LMD.

CONCLUSIONS

The outcomes of LMD patients in the BM population are predictable using SMOTE and machine learning. Lymph node metastasis of the primary tumor at BM diagnosis and a cerebellar BM location were the strongest LMD risk factors.

Deep convolutional neural network algorithm for the automatic segmentation of oral potentially malignant disorders and oral cancers

This study aimed to develop an algorithm to automatically segment the oral potentially malignant diseases (OPMDs) and oral cancers (OCs) of all oral subsites with various deep convolutional neural network applications. A total of 510 intraoral images of OPMDs and OCs were collected over 3 years (2006-2009). All images were confirmed both with patient records and histopathological reports. Following the labeling of the lesions the dataset was arbitrarily split, using random sampling in Python as the study dataset, validation dataset, and test dataset. Pixels were classified as the OPMDs and OCs with the OPMD/OC label and the rest as the background. U-Net architecture was used and the model with the best validation loss was chosen for the testing among the trained 500 epochs. Dice similarity coefficient (DSC) score was noted. The intra-observer ICC was found to be 0.994 while the inter-observer reliability was 0.989. The calculated DSC and validation accuracy across all clinical images were 0.697 and 0.805, respectively. Our algorithm did not maintain an excellent DSC due to multiple reasons for the detection of both OC and OPMDs in oral cavity sites. A better standardization for both 2D and 3D imaging (such as patient positioning) and a bigger dataset are required to improve the quality of such studies. This is the first study which aimed to segment OPMDs and OCs in all subsites of oral cavity which is crucial not only for the early diagnosis but also for higher survival rates.

An online survival predictor in glioma patients using machine learning based on WHO CNS5 data

Background

The World Health Organization (WHO) CNS5 classification system highlights the significance of molecular biomarkers in providing meaningful prognostic and therapeutic information for gliomas. However, predicting individual patient survival remains challenging due to the lack of integrated quantitative assessment tools. In this study, we aimed to design a WHO CNS5-related risk signature to predict the overall survival (OS) rate of glioma patients using machine learning algorithms.

Methods

We extracted data from patients who underwent an operation for histopathologically confirmed glioma from our hospital database (2011-2022) and split them into a training and hold-out test set in a 7/3 ratio. We used biological markers related to WHO CNS5, clinical data (age, sex, and WHO grade), and prognosis follow-up information to identify prognostic factors and construct a predictive dynamic nomograph to predict the survival rate of glioma patients using 4 kinds machine learning algorithms (RF, SVM, XGB, and GLM).

Results

A total of 198 patients with complete WHO5 molecular data and follow-up information were included in the study. The median OS time of all patients was 29.77 [95% confidence interval (CI): 21.19-38.34] months. Age, FGFR2, IDH1, CDK4, CDK6, KIT, and CDKN2A were considered vital indicators related to the prognosis and OS time of glioma. To better predict the prognosis of glioma patients, we constructed a WHO5-related risk signature and nomogram. The AUC values of the ROC curves of the nomogram for predicting the 1, 3, and 5-year OS were 0.849, 0.835, and 0.821 in training set, and, 0.844, 0.943, and 0.959 in validation set. The calibration plot confirmed the reliability of the nomogram, and the c-index was 0.742 in training set and 0.775 in validation set. Additionally, our nomogram showed a superior net benefit across a broader scale of threshold probabilities in decision curve analysis. Therefore, we selected it as the backend for the online survival prediction tool (Glioma Survival Calculator, https://who5pumch.shinyapps.io/DynNomapp/), which can calculate the survival probability for a specific time of the patients.

Conclusion

An online prognosis predictor based on WHO5-related biomarkers was constructed. This therapeutically promising tool may increase the precision of forecast therapy outcomes and assess prognosis.

A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data

Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability.

Prediction of model generalizability for unseen data: Methodology and case study in brain metastases detection in T1-Weighted contrast-enhanced 3D MRI

BACKGROUND AND PURPOSE

A medical AI system's generalizability describes the continuity of its performance acquired from varying geographic, historical, and methodologic settings. Previous literature on this topic has mostly focused on "how" to achieve high generalizability (e.g., via larger datasets, transfer learning, data augmentation, model regularization schemes), with limited success. Instead, we aim to understand "when" the generalizability is achieved: Our study presents a medical AI system that could estimate its generalizability status for unseen data on-the-fly.

MATERIALS AND METHODS

We introduce a latent space mapping (LSM) approach utilizing Fréchet distance loss to force the underlying training data distribution into a multivariate normal distribution. During the deployment, a given test data's LSM distribution is processed to detect its deviation from the forced distribution; hence, the AI system could predict its generalizability status for any previously unseen data set. If low model generalizability is detected, then the user is informed by a warning message integrated into a sample deployment workflow. While the approach is applicable for most classification deep neural networks (DNNs), we demonstrate its application to a brain metastases (BM) detector for T1-weighted contrast-enhanced (T1c) 3D MRI. The BM detection model was trained using 175 T1c studies acquired internally (from the authors' institution) and tested using (1) 42 internally acquired exams and (2) 72 externally acquired exams from the publicly distributed Brain Mets dataset provided by the Stanford University School of Medicine. Generalizability scores, false positive (FP) rates, and sensitivities of the BM detector were computed for the test datasets.

RESULTS AND CONCLUSION

The model predicted its generalizability to be low for 31% of the testing data (i.e., two of the internally and 33 of the externally acquired exams), where it produced (1) ∼13.5 false positives (FPs) at 76.1% BM detection sensitivity for the low and (2) ∼10.5 FPs at 89.2% BM detection sensitivity for the high generalizability groups respectively. These results suggest that the proposed formulation enables a model to predict its generalizability for unseen data.

Machine learning in metastatic cancer research: Potentials, possibilities, and prospects

Cancer has received extensive recognition for its high mortality rate, with metastatic cancer being the top cause of cancer-related deaths. Metastatic cancer involves the spread of the primary tumor to other body organs. As much as the early detection of cancer is essential, the timely detection of metastasis, the identification of biomarkers, and treatment choice are valuable for improving the quality of life for metastatic cancer patients. This study reviews the existing studies on classical machine learning (ML) and deep learning (DL) in metastatic cancer research. Since the majority of metastatic cancer research data are collected in the formats of PET/CT and MRI image data, deep learning techniques are heavily involved. However, its black-box nature and expensive computational cost are notable concerns. Furthermore, existing models could be overestimated for their generality due to the non-diverse population in clinical trial datasets. Therefore, research gaps are itemized; follow-up studies should be carried out on metastatic cancer using machine learning and deep learning tools with data in a symmetric manner.

Automatic detection of punctate white matter lesions in infants using deep learning of composite images from two cases

Punctate white matter lesions (PWMLs) in infants may be related to neurodevelopmental outcomes based on the location or number of lesions. This study aimed to assess the automatic detectability of PWMLs in infants on deep learning using composite images created from several cases. To create the initial composite images, magnetic resonance (MR) images of two infants with the most PWMLs were used; their PWMLs were extracted and pasted onto MR images of infants without abnormality, creating many composite PWML images. Deep learning models based on a convolutional neural network, You Only Look Once v3 (YOLOv3), were constructed using the training set of 600, 1200, 2400, and 3600 composite images. As a result, a threshold of detection probability of 20% and 30% for all deep learning model sets yielded a relatively high sensitivity for automatic PWML detection (0.908-0.957). Although relatively high false-positive detections occurred with the lower threshold of detection probability, primarily, in the partial volume of the cerebral cortex (≥ 85.8%), those can be easily distinguished from the white matter lesions. Relatively highly sensitive automatic detection of PWMLs was achieved by creating composite images from two cases using deep learning.

Diagnostic performance of magnetic resonance imaging-based machine learning in Alzheimer's disease detection: a meta-analysis

PURPOSE

Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI).

METHODS

The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias.

RESULTS

We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81-0.89), 0.88 (95%CI 0.84-0.91), 7.15 (95%CI 5.40-9.47), 0.17 (95%CI 0.12-0.22), 43.34 (95%CI 26.89-69.84), and 0.93 (95%CI 0.91-0.95).

CONCLUSION

ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.

A Deep Learning-Based Computer Aided Detection (CAD) System for Difficult-to-Detect Brain Metastases

PURPOSE

We sought to develop a computer-aided detection (CAD) system that optimally augments human performance, excelling especially at identifying small inconspicuous brain metastases (BMs), by training a convolutional neural network on a unique magnetic resonance imaging (MRI) data set containing subtle BMs that were not detected prospectively during routine clinical care.

METHODS AND MATERIALS

Patients receiving stereotactic radiosurgery (SRS) for BMs at our institution from 2016 to 2018 without prior brain-directed therapy or small cell histology were eligible. For patients who underwent 2 consecutive courses of SRS, treatment planning MRIs from their initial course were reviewed for radiographic evidence of an emerging metastasis at the same location as metastases treated in their second SRS course. If present, these previously unidentified lesions were contoured and categorized as retrospectively identified metastases (RIMs). RIMs were further subcategorized according to whether they did (+DC) or did not (-DC) meet diagnostic imaging-based criteria to definitively classify them as metastases based upon their appearance in the initial MRI alone. Prospectively identified metastases (PIMs) from these patients, and from patients who only underwent a single course of SRS, were also included. An open-source convolutional neural network architecture was adapted and trained to detect both RIMs and PIMs on thin-slice, contrast-enhanced, spoiled gradient echo MRIs. Patients were randomized into 5 groups: 4 for training/cross-validation and 1 for testing.

RESULTS

One hundred thirty-five patients with 563 metastases, including 72 RIMS, met criteria. For the test group, CAD sensitivity was 94% for PIMs, 80% for +DC RIMs, and 79% for PIMs and +DC RIMs with diameter <3 mm, with a median of 2 false positives per patient and a Dice coefficient of 0.79.

CONCLUSIONS

Our CAD model, trained on a novel data set and using a single common MR sequence, demonstrated high sensitivity and specificity overall, outperforming published CAD results for small metastases and RIMs - the lesion types most in need of human performance augmentation.

Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction

BACKGROUND

Rapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a good candidate for AI model development and achieved encouraging result in recent years. This article aimed at demonstrating current AI application in radiosurgery.

METHODS

Literatures published in PubMed during 2010-2022, discussing AI application in stereotactic radiosurgery were reviewed.

RESULTS

AI algorithms, especially machine learning/deep learning models, have been administered to different aspect of stereotactic radiosurgery. Spontaneous tumor detection and automated lesion delineation or segmentation were two of the promising application, which could be further extended to longitudinal treatment follow-up. Outcome prediction utilized machine learning algorithms with radiomic-based analysis was another well-established application.

CONCLUSIONS

Stereotactic radiosurgery has taken a lead role in AI development. Current achievement, limitation, and further investigation was summarized in this article.

Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis

Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022. Inclusion criteria were: patients with BMs; deep learning using MRI images was applied to detect the BMs; sufficient data were present in terms of detective performance; original research articles. Exclusion criteria were: reviews, letters, guidelines, editorials, or errata; case reports or series with less than 20 patients; studies with overlapping cohorts; insufficient data in terms of detective performance; machine learning was used to detect BMs; articles not written in English. Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Finally, 24 eligible studies were identified for the quantitative analysis. The pooled proportion of patient-wise and lesion-wise detectability was 89%. Articles should adhere to the checklists more strictly. Deep learning algorithms effectively detect BMs. Pooled analysis of false positive rates could not be estimated due to reporting differences.

Automated Detection of Brain Metastases on T1-Weighted MRI Using a Convolutional Neural Network: Impact of Volume Aware Loss and Sampling Strategy

BACKGROUND

Detection of brain metastases (BM) and segmentation for treatment planning could be optimized with machine learning methods. Convolutional neural networks (CNNs) are promising, but their trade-offs between sensitivity and precision frequently lead to missing small lesions.

HYPOTHESIS

Combining volume aware (VA) loss function and sampling strategy could improve BM detection sensitivity.

STUDY TYPE

Retrospective.

POPULATION

A total of 530 radiation oncology patients (55% women) were split into a training/validation set (433 patients/1460 BM) and an independent test set (97 patients/296 BM).

FIELD STRENGTH/SEQUENCE

1.5 T and 3 T, contrast-enhanced three-dimensional (3D) T1-weighted fast gradient echo sequences.

ASSESSMENT

Ground truth masks were based on radiotherapy treatment planning contours reviewed by experts. A U-Net inspired model was trained. Three loss functions (Dice, Dice + boundary, and VA) and two sampling methods (label and VA) were compared. Results were reported with Dice scores, volumetric error, lesion detection sensitivity, and precision. A detected voxel within the ground truth constituted a true positive.

STATISTICAL TESTS

McNemar's exact test to compare detected lesions between models. Pearson's correlation coefficient and Bland-Altman analysis to compare volume agreement between predicted and ground truth volumes. Statistical significance was set at P ≤ 0.05.

RESULTS

Combining VA loss and VA sampling performed best with an overall sensitivity of 91% and precision of 81%. For BM in the 2.5-6 mm estimated sphere diameter range, VA loss reduced false negatives by 58% and VA sampling reduced it further by 30%. In the same range, the boundary loss achieved the highest precision at 81%, but a low sensitivity (24%) and a 31% Dice loss.

DATA CONCLUSION

Considering BM size in the loss and sampling function of CNN may increase the detection sensitivity regarding small BM. Our pipeline relying on a single contrast-enhanced T1-weighted MRI sequence could reach a detection sensitivity of 91%, with an average of only 0.66 false positives per scan.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Background

Application of machine learning (ML) for identification of systemic lupus erythematosus (SLE) has been recently drawing increasing attention, while there is still lack of evidence-based support.

Methods

Systematic review and meta-analysis are conducted to evaluate its diagnostic accuracy and application prospect. PubMed, Embase, Cochrane Library, and Web of Science libraries are searched, in combination with manual searching and literature retrospection, for studies regarding machine learning for identifying SLE and neuropsychiatric systemic lupus erythematosus (NPSLE). Quality Assessment of Diagnostic Accuracy Studies (QUADA-2) is applied to assess the quality of included studies. Diagnostic accuracy of the SLE model and NPSLE model is assessed using the bivariate fixed-effect model, and the data are pooled. Summary receiver operator characteristic curve (SROC) is plotted, and area under the curve (AUC) is calculated.

Results

Eighteen (18) studies are included, in which ten (10) focused on SLE and eight (8) on NPSLE. The AUC of SLE identification is 0.95, the sensitivity is 0.90, the specificity is 0.89, the PLR is 8.4, the NLR is 0.12, and the DOR is 73. AUC of NPSLE identification is 0.89, the sensitivity is 0.83, the specificity is 0.83, the PLR is 5.0, the NLR is 0.20, and the DOR is 25.

Conclusion

Machine learning presented remarkable performance in identification of SLE and NPSLE. Based on the convenience for inclusion factor collection and non-invasiveness of detection, machine learning is expected to be widely applied in clinical practice to assist medical decision making.

A Self-Attention-Guided 3D Deep Residual Network With Big Transfer to Predict Local Failure in Brain Metastasis After Radiotherapy Using Multi-Channel MRI

A noticeable proportion of larger brain metastases (BMs) are not locally controlled after stereotactic radiotherapy, and it may take months before local progression is apparent on standard follow-up imaging. This work proposes and investigates new explainable deep-learning models to predict the radiotherapy outcome for BM. A novel self-attention-guided 3D residual network is introduced for predicting the outcome of local failure (LF) after radiotherapy using the baseline treatment-planning MRI. The 3D self-attention modules facilitate capturing long-range intra/inter slice dependencies which are often overlooked by convolution layers. The proposed model was compared to a vanilla 3D residual network and 3D residual network with CBAM attention in terms of performance in outcome prediction. A training recipe was adapted for the outcome prediction models during pretraining and training the down-stream task based on the recently proposed big transfer principles. A novel 3D visualization module was coupled with the model to demonstrate the impact of various intra/peri-lesion regions on volumetric multi-channel MRI upon the network's prediction. The proposed self-attention-guided 3D residual network outperforms the vanilla residual network and the residual network with CBAM attention in accuracy, F1-score, and AUC. The visualization results show the importance of peri-lesional characteristics on treatment-planning MRI in predicting local outcome after radiotherapy. This study demonstrates the potential of self-attention-guided deep-learning features derived from volumetric MRI in radiotherapy outcome prediction for BM. The insights obtained via the developed visualization module for individual lesions can possibly be applied during radiotherapy planning to decrease the chance of LF.

Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features

BACKGROUND

A considerable proportion of metastatic brain tumors progress locally despite stereotactic radiation treatment, and it can take months before such local progression is evident on follow-up imaging. Prediction of radiotherapy outcome in terms of tumor local failure is crucial for these patients and can facilitate treatment adjustments or allow for early salvage therapies.

PURPOSE

In this work, a novel deep learning architecture is introduced to predict the outcome of local control/failure in brain metastasis treated with stereotactic radiation therapy using treatment-planning magnetic resonance imaging (MRI) and standard clinical attributes.

METHODS

At the core of the proposed architecture is an InceptionResentV2 network to extract distinct features from each MRI slice for local outcome prediction. A recurrent or transformer network is integrated into the architecture to incorporate spatial dependencies between MRI slices into the predictive modeling. A visualization method based on prediction difference analysis is coupled with the deep learning model to illustrate how different regions of each lesion on MRI contribute to the model's prediction. The model was trained and optimized using the data acquired from 99 patients (116 lesions) and evaluated on an independent test set of 25 patients (40 lesions).

RESULTS

The results demonstrate the promising potential of the MRI deep learning features for outcome prediction, outperforming standard clinical variables. The prediction model with only clinical variables demonstrated an area under the receiver operating characteristic curve (AUC) of 0.68. The MRI deep learning models resulted in AUCs in the range of 0.72 to 0.83 depending on the mechanism to integrate information from MRI slices of each lesion. The best prediction performance (AUC = 0.86) was associated with the model that combined the MRI deep learning features with clinical variables and incorporated the inter-slice dependencies using a long short-term memory recurrent network. The visualization results highlighted the importance of tumor/lesion margins in local outcome prediction for brain metastasis.

CONCLUSIONS

The promising results of this study show the possibility of early prediction of radiotherapy outcome for brain metastasis via deep learning of MRI and clinical attributes at pre-treatment and encourage future studies on larger groups of patients treated with other radiotherapy modalities.

Principles of Lung Cancer Metastasis to Brain

Lung cancer is a disease associated with significant morbidity and mortality on a global setting. This form of cancer commonly gives raise to metastatic lesions the brain, which can further worsen outcomes. In this focused review, we discuss an overview of lung cancers that metastasize to the brain: known risk factors; means of detection and diagnosis; and options for treatment including a comparison between surgical resection, stereotactic radiosurgery, and whole-brain radiation therapy. These interventions are still being assessed by clinical trials and continue to be modified through evidence-based practice.

Small-cell lung cancer brain metastasis: From molecular mechanisms to diagnosis and treatment

Lung cancer is the most malignant human cancer worldwide, also with the highest incidence rate. However, small-cell lung cancer (SCLC) accounts for 14 % of all lung cancer cases. Approximately 10 % of patients with SCLC have brain metastasis at the time of diagnosis, which is the leading cause of death of patients with SCLC worldwide. The median overall survival is only 4.9 months, and a long-tern cure exists for patients with SCLC brain metastasis due to limited common therapeutic options. Recent studies have enhanced our understanding of the molecular mechanisms leading to meningeal metastasis, and multimodality treatments have brought new hopes for a better cure for the disease. This review aimed to offer an insight into the cellular processes of different metastatic stages of SCLC revealed by the established animal models, and into the major diagnostic methods of SCLC. Additionally, it provided in-depth information on the recent advances in SCLC treatments, and highlighted several new models and biomarkers with promises to improve the prognosis of SCLC.

Advancing Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI Using Noisy Student-Based Training

The detection of brain metastases (BM) in their early stages could have a positive impact on the outcome of cancer patients. The authors previously developed a framework for detecting small BM (with diameters of <15 mm) in T1-weighted contrast-enhanced 3D magnetic resonance images (T1c). This study aimed to advance the framework with a noisy-student-based self-training strategy to use a large corpus of unlabeled T1c data. Accordingly, a sensitivity-based noisy-student learning approach was formulated to provide high BM detection sensitivity with a reduced count of false positives. This paper (1) proposes student/teacher convolutional neural network architectures, (2) presents data and model noising mechanisms, and (3) introduces a novel pseudo-labeling strategy factoring in the sensitivity constraint. The evaluation was performed using 217 labeled and 1247 unlabeled exams via two-fold cross-validation. The framework utilizing only the labeled exams produced 9.23 false positives for 90% BM detection sensitivity, whereas the one using the introduced learning strategy led to ~9% reduction in false detections (i.e., 8.44). Significant reductions in false positives (>10%) were also observed in reduced labeled data scenarios (using 50% and 75% of labeled data). The results suggest that the introduced strategy could be utilized in existing medical detection applications with access to unlabeled datasets to elevate their performances.