Deep intracranial tumors situated in eloquent brain regions controlling vital functions present critical diagnostic challenges. Clinical practice has shifted toward stereotactic biopsy for pathological confirmation before treatment. Yet biopsy carries inherent risks of hemorrhage and neurological deficits and struggles with sampling bias due to tumor spatial heterogeneity, because pathological changes are typically region-selective rather than tumor-wide. Therefore, advancing non-invasive MRI-based pathology prediction is essential for holistic tumor assessment and modern clinical decision-making.
The primary challenge lies in data scarcity: low tumor incidence requires long collection cycles, and annotation demands biopsy-verified pathology from neurosurgical experts. Additionally, tiny lesion volumes lacking segmentation masks cause critical features to be overwhelmed by background noise. To address these challenges, we construct the ICT-MRI dataset - the first public biopsy-verified benchmark with 249 cases across four categories. We propose a Virtual Biopsy framework comprising: MRI-Processor for standardization; Tumor-Localizer employing vision-language models for coarse-to-fine localization via weak supervision; and Adaptive-Diagnoser with a Masked Channel Attention mechanism fusing local discriminative features with global contexts. Experiments demonstrate over 90% accuracy, outperforming baselines by more than 20%.
Beijing Chao-Yang Hospital（北京朝阳医院）；Beijing University of Posts and Telecommunications（北京邮电大学）
Objectives: To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) …
Indian Institute of Technology, Bombay（印度理工学院孟买分校）；Tata Memorial Centre-ACTREC, HBNI（霍米·巴巴国立研究所塔塔纪念中心-先进治疗研究与教育中心）；Tata Memorial Centre-TMH, HBNI（霍米·巴巴国立研究所塔塔纪念中心-塔塔纪念医院）
The standard diagnostic procedures for targeted therapies in lung cancer treatment involve histological subtyping and subsequent detection of key driver mutations, such as EGFR. Even though molecular profiling can uncover the driver mutation, the process is often expensive and time-consuming. Deep learning-oriented image analysis offers a more economical alternative for discovering driver mutations directly from whole slide images (WSIs). In this work, we used customized deep learning pipelines with weak supervision to identify the morphological correlates of EGFR mutation from hematoxylin and…
Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）；Boston University School of Medicine（波士顿大学医学院）；VA Boston Healthcare（波士顿退伍军人医疗中心）
Histopathological diagnoses of tumors in tissue biopsy after Hematoxylin and Eosin (H&E) staining is the gold standard for oncology care. H&E staining is slow and uses dyes, reagents and precious tissue samples that cannot be reused. Thousands of native nonstained RGB Whole Slide Image (RWSI) patches of prostate core tissue biopsies were registered with their H&E stained versions. Conditional Generative Adversarial Neural Networks (cGANs) that automate conversion of native nonstained RWSI to computational H&E stained images were then trained. High similarities between computational and H&E dye…
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Duke University Medical Center（杜克大学医学中心）；Crestview Radiology（远景放射科）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University of Pennsylvania（宾夕法尼亚大学）；Missouri University（密苏里大学）；Children's National Hospital（国家儿童医院）；George Washington University（乔治华盛顿大学）；Sage Bionetworks（赛智生物网络公司）；University of California San Francisco（加州大学旧金山分校）；Mayo Clinic（梅奥诊所）；Technical University Munich（慕尼黑工业大学）；Technical University of Munich（慕尼黑工业大学）；Children’s Hospital of Philadelphia（费城儿童医院）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）；Massachusetts General Hospital（马萨诸塞州总医院）；U.S. Food and Drug Administration（美国食品药品监督管理局）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；Technical University of Denmark（丹麦技术大学）；University of Zurich（苏黎世大学）；Thomas Jefferson University（托马斯杰斐逊大学）；Yale University（耶鲁大学）；Booz Allen Hamilton（博思艾伦咨询公司）；Mercy Catholic Medical Center（仁慈天主教医疗中心）；Northwestern University（西北大学）；McGill University（麦吉尔大学）；University of San Diego（圣地亚哥大学）
Beihang University（北京航空航天大学）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）；Shanghai Jiao Tong University（上海交通大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）
In the clinical diagnosis and treatment of brain tumors, manual image reading consumes a lot of energy and time. In recent years, the automatic tumor classification technology based on deep learning has entered people's field of vision. Brain tumors can be divided into primary and secondary intracranial tumors according to their source. However, to our best knowledge, most existing research on brain tumors are limited to primary intracranial tumor images and cannot classify the source of the tumor. In order to solve the task of tumor source type classification, we analyze the existing technolo…
University of Minho（米尼奥大学）；University Hospital Inselspital（因塞尔施皮塔尔大学医院）；University of Bern（伯尔尼大学）
Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with patients having a poor prognosis. Lower grade gliomas are less aggressive, but they can evolve into higher grade tumors over time. Patient management and treatment can vary considerably with tumor grade, ranging from tumor resection followed by a combined radio- and chemotherapy to a "wait and see" approach. Hence, tumor grading is important for adequate treatment planning and monitoring. The gold standard for tumor grading relies on histopathological diagnosis of biopsy specimens. However, this procedure is invasive, …
Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence
Department of Radiology, Fondazione Policlinico Campus Bio-Medico（放射学系，政策临床医学院）；Department of Radiology, Università Campus Bio-Medico di Roma（放射学系，罗马生物医学大学）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与介入系，辐射物理，生物医学工程，乌梅拉大学）
A Multi-Stage Deep Learning Framework with PKCP-MixUp Augmentation for Pediatric Liver Tumor Diagnosis Using Multi-Phase Contrast-Enhanced CT
Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院武汉儿童医院（武汉市妇幼保健院））；Wuhan University（武汉大学）；Wuhan Cloud Computing Technology Co., Ltd.（武汉云计算技术有限公司）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Kharkevich Institute for Information Transmission Problems（哈尔克维奇信息传输问题研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Moscow Gamma-Knife Center（莫斯科伽马刀中心）；Burdenko Neurosurgery Institute（布尔坚科神经外科研究所）
Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors. As part of the therapy treatment, accurate delineation of brain tumors is of great importance. However, slice-by-slice manual segmentation on T1c MRI could be time-consuming (especially for multiple metastases) and subjective. In our work, we compared several deep convolutional networks architectures and training procedures and evaluated the best model in a radiation therapy department for three types of brain tumors: meningiomas, schwannomas and multiple brain metastases…
Isfahan University of Technology（伊斯法罕理工大学）；Seattle University（西雅图大学）；McMaster University（麦克马斯特大学）
According to the World Health Organization (WHO), cancer is the second leading cause of death worldwide, responsible for over 9.5 million deaths in 2018 alone. Brain tumors count for one out of every four cancer deaths. Therefore, accurate and timely diagnosis of brain tumors will lead to more effective treatments. Physicians classify brain tumors only with biopsy operation by brain surgery, and after diagnosing the type of tumor, a treatment plan is considered for the patient. Automatic systems based on machine learning algorithms can allow physicians to diagnose brain tumors with noninvasive…
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Duke University Medical Center（杜克大学医学中心）；Indiana University School of Medicine（印第安纳大学医学院）；Indiana University（印第安纳大学）；Thomas Jefferson University（托马斯杰斐逊大学）；University of California San Francisco（加利福尼亚大学旧金山分校）；Yale University（耶鲁大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University of Missouri（密苏里大学）；Children’s Hospital of Philadelphia, University of Pennsylvania（宾夕法尼亚大学费城儿童医院）；University of Pennsylvania（宾夕法尼亚大学）；Helmholtz Munich（亥姆霍兹慕尼黑）；Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；University of California San Francisco (UCSF)（加利福尼亚大学旧金山分校）
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal. The effectiveness of chemotherapy, being the standard treatment for most cancer types, can be improved if a particular genetic sequence in the tumor known as MGMT promoter is methylated. However, to identify the state of the MGMT promoter, the conventional approach is to perform a biopsy for genetic analysis, which is time and effort consuming. A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use o…
Hamburg University of Technology（汉堡工业大学）；Carl von Ossietzky University Oldenburg（奥尔登堡大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；University of Bonn（波恩大学）；Klinikum Braunschweig（不伦瑞克医院）
Early detection of cancerous tissue is crucial for long-term patient survival. In the head and neck region, a typical diagnostic procedure is an endoscopic intervention where a medical expert manually assesses tissue using RGB camera images. While healthy and tumor regions are generally easier to distinguish, differentiating benign and malignant tumors is very challenging. This requires an invasive biopsy, followed by histological evaluation for diagnosis. Also, during tumor resection, tumor margins need to be verified by histological analysis. To avoid unnecessary tissue resection, a non-inva…
The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively captur…
Indian Statistical Institute（印度统计研究所）；University of Calcutta（加尔各答大学）；University of Genova（热那亚大学）
Glioma constitutes 80% of malignant primary brain tumors and is usually classified as HGG and LGG. The LGG tumors are less aggressive, with slower growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy being challenging for brain tumor patients, noninvasive imaging techniques like Magnetic Resonance Imaging (MRI) have been extensively employed in diagnosing brain tumors. Therefore automated systems for the detection and prediction of the grade of tumors based on MRI data becomes necessary for assisting doctors in the framework of augmented intelligence. In this paper, we t…
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital（谢赫·扎耶德儿科外科创新研究所，儿童国家医院）；CIBER-BBN, ISCIII（西班牙生物医学研究中心-生物工程、生物材料与纳米医学分部，卡洛斯三世卫生研究所）；Princeton University（普林斯顿大学）；School of Medicine and Health Sciences, George Washington University（乔治·华盛顿大学医学与健康科学学院）
Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València（瓦伦西亚理工大学生物工程研究与创新研究所）；Pathology Department. Hospital Clínico Universitario de Valencia, Universidad de Valencia（瓦伦西亚大学临床大学医院病理科）
Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging melanocytic lesions due to their ambiguous morphological features. The gold standard for its diagnosis and prognosis is the analysis of skin biopsies. In this process, dermatopathologists visualize skin histology slides under a microscope, in a high time-consuming and subjective task. In the last years, computer-aided diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in daily clinical practic…
Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
Amsterdam UMC, Department of Radiation Oncology（阿姆斯特丹大学医学中心放射肿瘤科）；Cancer Center Amsterdam, Imaging and Biomarkers（阿姆斯特丹癌症中心影像与生物标志物）；Department of Radiology and Nuclear Medicine（放射科与核医学科）；Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Brain Imaging（阿姆斯特丹大学医学中心、阿姆斯特丹自由大学、阿姆斯特丹神经科学、脑成像）
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI
School of Computer Science and Engineering, Vellore Institute of Technology（韦洛尔理工学院计算机科学与工程学院）；College of Engineering, Guindy, Anna University（安娜大学金迪工程学院）；Institute of Neurosurgery, Madras Medical College（马德拉斯医学院神经外科研究所）
Washington University in St. Louis（圣路易斯华盛顿大学）；Mallinckrodt Institute of Radiology（马林克罗特放射研究所）；Washington University School of Medicine（华盛顿大学医学院）；Institute for Informatics（信息学研究所）
Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. For this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade II-IV) were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy…
Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video
National Yang Ming Chiao Tung University（国立阳明交通大学）；National Taiwan University Cancer Center（台湾大学癌症中心）；National Taiwan University Hospital（台湾大学医院）；National Taiwan University Hsin-Chu Hospital（台湾大学新竹医院）
University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine（佩雷尔曼医学院）
In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Previous work on characterizing the infiltrative heterogeneity in the peritumoral region used various imaging modalities, but information of extracellular free water movement restriction has been limitedly explored. Here, we derive a unique set of Artificial Intelligence (AI)-based mar…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Wake Forest School of Medicine（维克森林大学医学院）
Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MRI data. Our IRB-approved single-site retrospective study was comprised of patients (n=1,399) referred for MRI treatment-planning and gamma knife radiosurgery over 21 years. Contrast-enhanced T1-weighted and T2-weighted Fluid-Attenuated Inversion Recovery brain MRI exams (n=1,582) were preprocessed and input to the proposed deep learning workflow fo…
