This dataset provides lung cancer MRI images along with associated clinical and genomic features for all patients. Each patient entry includes:

Clinical Features:

Histological type (Adenocarcinoma, Squamous cell carcinoma, Large cell carcinoma, Small cell lung carcinoma)

Tumor grade (G1–G4)

Tumor tissue site, laterality, and tumor location

Patient demographics: age at initial pathology, gender, race, ethnicity

Mortality status (death01)

Gene Cluster Annotations:

TP53Cluster (TP53_1 to TP53_5)

EGFRCluster (EGFR_1 to EGFR_5)

CDKN2ACluster (CDKN2A_1α, CDKN2A_1β, CDKN2A_2)

KRAS G12C Cluster (KRAS G12C_2, KRAS G12C_3)

EML4-ALKCluster (EML4-ALK_1, EML4-ALK_3a, EML4-ALK_3b, EML4-ALK_2, EML4-ALK_5)

The respective data is comprised of 5 different datasets of medical images collected by the contributors, which can be used for classifying Lung Cancer, Bone Fracture, Brain tumor, Skin Lesions, and Renal Malignancy, respectively. The data also includes multiple disease and malignancy images for the respective dataset. The classification for the diseases can be done by using ResNet50 CNN architecture and other DCNN models. This data is also been used in a research article by the contributor.

Quantitative imaging biomarkers (QIB) are increasingly used in clinical research to advance precision medicine approaches in oncology. Unlike biopsy-based biomarkers, QIBs are non-invasive and can estimate the spatial and temporal heterogeneity of total tumor burden. Computed tomography (CT) is a modality of choice for cancer diagnosis, prognosis, and response assessment due to its reliability and global accessibility.

In recent years, despite overwhelmingly increased awareness of the reproducibility and robustness in quantitative imaging studies, lack of precious clinical image data limits our investigation and algorithm development. Here, we contribute to the cancer imaging community with our investigator-initiated, same-day repeat CT scan images of 32 non–small cell lung cancer (NSCLC) patients, along with radiologist’s annotated lesion contours as the reference standard. Each scan was reconstructed into 6 image settings using various combinations of three slice thicknesses (1.25 mm, 2.5 mm, 5 mm) and two reconstruction kernels (lung, standard; GE CT equipment), which spans a wide range of CT imaging reconstruction parameters commonly used in lung cancer clinical practice and clinical trials. One of the 6-settings, i.e., the setting of 1.25mm slice thickness and lung reconstruction (1.25L), was published as part of the Reference Image Database to Evaluate Therapy Response (RIDER) project in 2012.

We believe that this entire dataset, comprising CT lung cancer images reconstructed on the same day at six different image settings, holds considerable value for advancing the development of robust Artificial Intelligence (AI) and machine learning (ML) methods. Additionally, it provides a valuable resource for comparing QIBs derived from a wide range of CT imaging parameter settings, for investigating data harmonization approaches, and for identifying specific CT imaging parameters most suitable for studying radiomics in lung cancer.

Context

Zhao, B., James, L. P., Moskowitz, C. S., Guo, P., Ginsberg, M. S., Lefkowitz, R. A., … Schwartz, L. H. (2009, July). Evaluating Variability in Tumor Measurements from Same-day Repeat CT Scans of Patients with Non–Small Cell Lung Cancer 1 . Radiology. Radiological Society of North America (RSNA). http://doi.org/10.1148/radiol.2522081593 (paper)

https://wiki.cancerimagingarchive.net/display/Public/RIDER+Lung+CT

The Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat "coffee break," longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT] positron emission tomography [PET] CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):

Content

The long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the first RIDER white paper report (2006):

Acknowledgements

https://wiki.cancerimagingarchive.net/display/Public/RIDER+Lung+CT

Zhao, Binsheng, Schwartz, Lawrence H, & Kris, Mark G. (2015). Data From RIDER_Lung CT. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.U1X8A5NR

Inspiration

Advance BioMedical Image Data Science.

Supplementary Material 1

The use of prophylactic cranial irradiation (PCI) in patients suffering from limited-stage small-cell lung cancer (LS-SCLC) remains controversial in modern brain magnetic resonance imaging (MRI) staging. To this end, a systematic review with meta-analysis was hereby performed to investigate the overall survival (OS) in these patients. Relevant studies from the PubMed and EMBASE databases were reviewed, and pooled hazard risks were obtained using fixed-effects models. The PRISMA 2020 checklist was used. Fifteen retrospective studies were identified, with a total of 2,797 patients with LS-SCLC included in the analysis, 1,391 of which had received PCI. For all included patients, PCI was associated with improved OS [hazard ratio (HR): 0.64, 95% confidence interval (CI): 0.58–0.70]. The combination of subgroup analysis and sensitivity analysis suggested that the effect of PCI on OS was independent of primary tumor treatment, proportion of complete response (CR), median age, PCI dose, publication years, etc. Additionally, the OS curve of 1,588 patients having undergone thoracic radiotherapy (TRT) as the primary tumor treatment from 8 studies were reconstructed, and the pooled 2-, 3- and 5-year OS rates of limited stage patients were 59% vs. 42%, 42% vs. 29% and 26% vs. 19% (HR: 0.69, 95% CI: 0.61–0.77) in the PCI group and the no PCI group, respectively. Another reconstructed OS curve of 339 patients having undergone radical surgery as the primary tumor treatment from 2 studies presented better results, and the pooled 2-, 3- and 5-year OS rates of in the PCI group and the no PCI group were 85% vs. 71%, 70% vs. 56% and 52% vs. 39% (HR: 0.59, 95% CI: 0.40–0.87), respectively. This meta-analysis demonstrates a significant beneficial effect of PCI on the OS in patients with LS-SCLC in modern pretreatment MRI staging. However, considering the absence of a strict follow-up of brain MRI recommended by the guideline for the control group from most of the included studies, the superiority of PCI to the treatment strategy of no PCI plus brain MRI surveillance remains unclear.

These data contain 834 extracted radiomic features from 17 non-small-cell lung cancer patients, each undergoing a 20-minute 18F-fluorodeoxyglucose positron emission tomography (PET)/magnetic resonance imaging (MRI) acquisition. Features from three PET image reconstructions were extracted: 1) free-breathing PET (PET_100%), 2) end-expiration PET (PET_40%) and 3) MRI-based motion corrected PET (PET_MoCo).

Objective: The aim of this study was to prospectively evaluate the feasibility of monitoring treatment response to chemotherapy in patients with non-small cell lung carcinoma using functional diffusion maps (fDMs). Materials and Methods: This study was approved by the Cantonal Research Ethics Committee and informed written consent was obtained from all patients. Nine patients (mean age = 66 years; range = 53–76 years, 5 females, 4 males) with overall 13 lesions were included. Imaging was performed within two weeks before initiation of chemotherapy and at one, two, and six weeks after initiation of chemotherapy. Imaging included a respiratory-triggered diffusion-weighted sequence including three b-factors (100, 600, and 800 s/mm2). Treatment response was defined by change in tumor diameter on computed tomography (CT) after two cycles of chemotherapy. Changes in the apparent diffusion coefficient (ADC) on a per-lesion basis and the percentages of voxel with significantly increased or decr...

IntroductionRegarding whether brain magnetic resonance imaging (MRI) should be routine in patients with suspected early-stage lung cancer, guideline recommendations are inconsistent. Therefore, we performed this study to evaluate the incidence of and risk factors for brain metastasis (BM) in patients with suspected early-stage non-small-cell lung cancer (NSCLC).MethodsA review of the medical charts of consecutive NSCLC patients diagnosed between January 2006 and May 2020 was performed. We identified 1,382 NSCLC patients with clinical staging of T1/2aN0M0 (excluding BM), and investigated the incidence, clinical predictors, and prognosis of BM in the cohort. We also performed RNA-sequencing differential expression analysis using transcriptome of 8 patients, using DESeq2 package (version 1.32.0) with R (version 4.1.0).ResultsAmong 1,382 patients, nine hundred forty-nine patients (68.7%) underwent brain MRI during staging, and 34 patients (3.6%) were shown to have BM. Firth’s bias-reduced logistic regression showed that tumor size (OR 1.056; 95% CI 1.009-1.106, p=0.018) was the only predictor of BM, and pathologic type was not a predictor of BM in our cohort (p>0.05). The median overall survival for patients with brain metastasis was 5.5 years, which is better than previously reported in the literature. RNA-sequencing differential expression analysis revealed the top 10 significantly upregulated genes and top 10 significantly downregulated genes. Among the genes involved in BM, Unc-79 homolog, non-selective sodium leak channel (NALCN) channel complex subunit (UNC79) was the most highly expressed gene in the lung adenocarcinoma tissues from the BM group, and an in vitro assay using A549 cells revealed that the NALCN inhibitor suppressed lung cancer cell proliferation and migration.ConclusionsGiven the incidence and favorable outcome of BM in patients with suspected early-stage NSCLC, selective screening with brain MRI may be considered, especially in patients with high-risk features.

Raw Dataset: Md Shahriar Mannan, Prottoy; Chowdhury , Mahtab ; Rahman, Redwan ; Tamim , Azim Ullah ; Rahman, Md Mizanur (2024), “PMRAM: Bangladeshi Brain Cancer - MRI Dataset ”, Mendeley Data, V1, doi: 10.17632/m7w55sw88b.1

Mixed: https://www.kaggle.com/datasets/shuvokumarbasak2030/pmram-bangladeshi-brain-cancer-colorized-mri-data/data More: https://www.kaggle.com/datasets/shuvokumarbasakbd/pmram-bangladeshi-brain-cancer-mri-colorized-data

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F0c48eb8826cf53d0a6993b72ef2a1c96%2FScreenshot%20(304).png?generation=1763651373014818&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F3b3962419420c77a03ff83680278cd4f%2FScreenshot%20(305).png?generation=1763651392161286&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F612341b51ebd147f772bd053adfafd20%2FScreenshot%20(306).png?generation=1763651408416813&alt=media" alt=""> This script implements 53 image processing and color augmentation techniques applied to medical imaging datasets (CT and MRI), particularly for visualization, preprocessing, or data augmentation for deep learning models.

The methods list includes:

1-13: Original visualization methods ("Basic_Color_Map", Basic_Color_Map) - Simple colormap (Jet) mapping. ("Adaptive_Histogram_Equalization", Adaptive_Histogram_Equalization) - Enhances local contrast using CLAHE. Used in many radiology preprocessing pipelines. ("Contrast_Stretching", Contrast_Stretching) - Stretches intensity range for better visualization. ("Gaussian_Blur", Gaussian_Blur) - Smooths image, reduces noise. ("Edge_Detection", Edge_Detection) - Detects edges using Canny algorithm. ("Random_Color_Palette", Random_Color_Palette) - Random color overlays for visualization/augmentation. ("Gamma_Correction", Gamma_Correction) - Non-linear intensity adjustment. ("LUT_Color_Map", LUT_Color_Map) - Colormap using pre-defined lookup tables (Plasma). ("Alpha_Blending", Alpha_Blending) - Combines threshold mask with colormap for highlighting regions. ("Render_3D", Render_3D) - Placeholder for 3D visualization of 2D slices. ("Heatmap_Visualization", Heatmap_Visualization) - Hot colormap visualization for anomalies. ("Volume_Render_3D", Volume_Render_3D) - Placeholder for 3D volume rendering. ("Interactive_Segmentation", Interactive_Segmentation) - Highlights segmented regions based on intensity thresholds.

14-33: CT/MRI-specific windowing, noise, and enhancement techniques ("CT_Soft_Tissue", CT_Soft_Tissue) - Windowing for soft tissue visualization (e.g., abdomen). ("CT_Bone", CT_Bone) - Bone window for skeletal visualization. ("CT_Lung", CT_Lung) - Lung window for lung tissue evaluation. ("CT_Brain", CT_Brain) - Brain window for head CT. ("MRI_T1", MRI_T1) - T1-weighted MRI contrast enhancement. ("MRI_T2", MRI_T2) - T2-weighted MRI contrast enhancement. ("MRI_FLAIR", MRI_FLAIR) - FLAIR MRI highlighting lesions. ("CT_Window_Sweep", CT_Window_Sweep) - Randomized window sweep for augmentation. ("Histogram_Match_MRI", Histogram_Match_MRI) - Histogram matching for intensity normalization. ("Zscore_Normalization", Zscore_Normalization) - Normalization by mean/std. ("Rician_Noise", Rician_Noise) - Simulated MRI Rician noise. ("Poisson_Noise", Poisson_Noise) - Simulated Poisson noise. ("Gaussian_Noise", Gaussian_Noise) - Gaussian additive noise. ("Speckle_Noise", Speckle_Noise) - Speckle noise simulation. ("Motion_Blur", Motion_Blur) - Simulates patient motion. ("Gibbs_Ringing", Gibbs_Ringing) - Simulates ringing artifacts. ("Laplacian_Sharpen", Laplacian_Sharpen) - Enhances edges using Laplacian filter. ("Vesselness_Frangi", Vesselness_Frangi) - Enhances vascular structures (Frangi filter). ("CT_Window_Heatmap", CT_Window_Heatmap) - Heatmap over windowed CT intensities. ("Lesion_Highlight", Lesion_Highlight) - Highlights lesions based on threshold.

34-53: General image augmentation techniques used for data diversity ("Brightness_Jitter", Brightness_Jitter) - Random brightness adjustment. ("Contrast_Jitter", Contrast_Jitter) - Random contrast adjustment. ("Color_Temperature", Color_Temperature) - Simulates warm/c...

Archive of medical images of cancer accessible for public download. All images are stored in DICOM file format and organized as Collections, typically patients related by common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. Neuroimaging data sets include clinical outcomes, pathology, and genomics in addition to DICOM images. Submitting Data Proposals are welcomed.

The dataset used in this paper is a collection of lung cancer pathology images and brain tumor MRI images.

Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being a non-invasive procedure, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available for biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, segmentation maps of tumors in the CT scans, and quantitative values obtained from the PET/CT scans. Imaging data are also paired with gene mutation, RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between genomic and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.

Further details regarding this data-set may be found in Bakr, et. al, Sci Data. 2018 Oct 16;5:180202. doi: 10.1038/sdata.2018.202, https://www.ncbi.nlm.nih.gov/pubmed/30325352.

For scientific and other inquiries about this dataset, please contact TCIA's Helpdesk.

Purpose: To design and validate a preprocessing procedure dedicated to T2-weighted MR images of lung cancers so as to improve the ability of radiomic features to distinguish between adenocarcinoma and other histological types.Materials and Methods: A discovery set of 52 patients with advanced lung cancer who underwent T2-weighted MR imaging at 3 Tesla in a single center study from August 2017 to May 2019 was used. Findings were then validated using a validation set of 19 additional patients included from May to October 2019. Tumor type was obtained from the pathology report after trans-thoracic needle biopsy, metastatic lymph node or metastasis samples, or surgical excisions. MR images were preprocessed using N4ITK bias field correction and by normalizing voxel intensities with fat as a reference region. Segmentation and extraction of radiomic features were performed with LIFEx software on the raw images, on the N4ITK-corrected images and on the fully preprocessed images. Two analyses were conducted where radiomic features were extracted: (1) from the whole tumor volume (3D analysis); (2) from all slices encompassing the tumor (2D analysis). Receiver operating characteristic (ROC) analysis was used to identify features that could distinguish between adenocarcinoma and other histological types. Sham experiments were also designed to control the number of false positive findings.Results: There were 31 (12) adenocarcinomas and 21 (7) other histological types in the discovery (validation) set. In 2D, preprocessing increased the number of discriminant radiomic features from 8 without preprocessing to 22 with preprocessing. 2D analysis yielded more features able to identify adenocarcinoma than 3D analysis (12 discriminant radiomic features after preprocessing in 3D). Preprocessing did not increase false positive findings as no discriminant features were identified in any of the sham experiments. The greatest sensitivity of the 2D analysis applied to preprocessed data was confirmed in the validation set.Conclusion: Correction for magnetic field inhomogeneities and normalization of voxel values are essential to reveal the full potential of radiomic features to identify the tumor histological type from MR T2-weighted images, with classification performance similar to those reported in PET/CT and in multiphase CT in lung cancers.

Medical Imaging (CT-Xray) Colorization New Dataset 🩺💻🖼️ This dataset provides a collection of medical imaging data, including both CT (Computed Tomography) and X-ray images, with an added focus on colorization techniques. The goal of this dataset is to facilitate the enhancement of diagnostic processes by applying various colorization techniques to grayscale medical images, allowing researchers and machine learning models to explore the effects of color in radiology.

Key Features: CT and X-ray Images 🏥: Contains both CT scans and X-ray images, widely used in medical diagnostics. Colorized Medical Images 🌈: Each image has been colorized using advanced methods to improve visual interpretation and analysis, including details that might not be immediately obvious in grayscale images. New Dataset 📊: This dataset is newly created to provide high-quality colorized medical imaging, ideal for training AI models in medical image analysis and enhancing diagnostic accuracy. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F4bfb7257cf09b0a118808b289c6c3ed4%2Fmotion_image.gif?generation=1742292037458801&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F20c64287d3b580a36bf8f948f82dbb6b%2Fmotion_image2.gif?generation=1742292060396551&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2Fdb91cac64f5a6a9100ac117fc8a55ee5%2Fmotion_image4.gif?generation=1742292150147491&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F8624a8cab05645e3a5f02a2c1e3e9e3f%2Fmotion_image3.gif?generation=1742292165846162&alt=media" alt="">

Methods Used for Colorization: Basic Color Map Application 🎨: Applying standard color maps to highlight structures in CT and X-ray images. Adaptive Histogram Equalization (CLAHE) 🔍: Adaptive enhancement to improve contrast and highlight important features, especially in medical contexts. Contrast Stretching 📈: Adjusting image intensity to enhance visual details and improve diagnostic quality. Gaussian Blur 🌀: Applied to reduce noise, offering a smoother image for better processing. Edge Detection (Canny) ✨: Detecting edges and contours, useful for identifying specific features in medical scans. Random Color Palettes 🎨: Using randomized color schemes for unique visual representations. Gamma Correction 🌟: Adjusting image brightness to reveal more information hidden in the shadows. LUT (Lookup Table) Color Mapping 💡: Applying predefined color lookups for visually appealing representations. Alpha Blending 🔶: Blending colorized regions based on certain thresholds to highlight structures or anomalies. 3D Rendering 🔺: For creating 3D-like visualizations from 2D scans. Heatmap Visualization 🔥: Highlighting areas of interest, such as anomalies or tumors, using heatmap color gradients. Interactive Segmentation 🖱️: Interactive visualizations that help in segmenting regions of interest in medical images. Applications 🏥💡 This dataset has numerous applications, particularly in the field of medical image analysis, AI development, and diagnostic improvement. Some of the major applications include:

Medical Diagnostics Enhancement 🔍:

Colorization can aid radiologists in interpreting CT and X-ray images by making abnormalities more visible. Helps in visualizing tumors, fractures, or other anomalies, especially in cases where grayscale images are hard to interpret. AI and Machine Learning for Healthcare 🤖:

Used for training deep learning models in image segmentation, detection, and classification of diseases (e.g., cancer detection). AI models can be trained on these colorized images to improve accuracy in diagnostic tools, leading to early disease detection. Medical Image Enhancement 🖼️:

Enables improved contrast, better detail visibility, and highlighting of specific anatomical regions using color. Colorization may improve the accuracy of radiological assessments by allowing professionals to more easily spot abnormalities and changes over time. Data Augmentation for Model Training 📚:

The colorized images can serve as an additional data source for training AI models, increasing model robustness through synthetic data generation. Various colorization methods (like heatmaps and random palettes) can be used to augment image variations, improving model performance under different conditions. Visualizing Anomalies for Anomaly Detection 🔥:

Heatmap visualization helps detect subtle and hidden anomalies by coloring the areas of interest with intensity, enabling faster identification of potential issues. Edge detection and segmentation techniques enhance the ability to detect the edges and boundaries of tumors, fractures, and other critical features. 3D Image Rendering for Detailed Analysis 🧠:

3D rend...

The global cancer testing and screening market is experiencing robust growth, driven by rising cancer incidence rates, advancements in diagnostic technologies, and increasing awareness among the population. This market, currently estimated at $50 billion in 2025, is projected to exhibit a compound annual growth rate (CAGR) of 7% from 2025 to 2033. This significant expansion is fueled by several key factors. The increasing adoption of advanced screening methods like Magnetic Resonance Imaging (MRI) and Human Papillomavirus (HPV) testing, coupled with the rising prevalence of various cancer types such as lung, blood, and bone cancers, significantly contributes to market growth. Furthermore, technological advancements leading to more accurate, faster, and less invasive diagnostic procedures are driving adoption rates. Government initiatives promoting early cancer detection and screening programs also play a crucial role. However, challenges like high costs associated with advanced testing, limited access to these technologies in underdeveloped regions, and the complexity of some screening procedures act as restraints to broader market penetration. The market segmentation reveals a diverse landscape. MRI holds a substantial share within the technology segment due to its high diagnostic accuracy. HPV testing dominates the screening segment for cervical cancer, while colonoscopy remains a crucial tool for colorectal cancer screening. Lung cancer constitutes a significant portion of the application segment, reflecting its high prevalence. Key players like Diasorin, Epigenetics, and Abbott Laboratories are shaping the market through innovation, acquisitions, and strategic partnerships. Geographical analysis indicates strong growth in North America and Europe, driven by advanced healthcare infrastructure and high healthcare expenditure. However, emerging markets in Asia-Pacific are expected to show substantial growth in the coming years, fueled by rising disposable incomes and improving healthcare access. The forecast period of 2025-2033 promises further expansion, driven by ongoing technological advancements and rising awareness regarding cancer prevention and early detection.

The RIDER Lung PET-CT collection was shared to facilitate the RIDER PET/CT subgroup activities. The PET/CT subgroup was responsible for: (1) archiving de-identified DICOM serial PET/CT phantom and lung cancer patient data in a public database to provide a resource for the testing and development of algorithms and imaging tools used for assessing response to therapy, (2) conducting multiple serial imaging studies of a long half-life phantom to assess systemic variance in serial PET/CT scans that is unrelated to response, and (3) identifying and recommending methods for quantifying sources of variance in PET/CT imaging with the goal of defining the change in PET measurements that may be unrelated to response to therapy, thus defining the absolute minimum effect size that should be used in the design of clinical trials using PET measurements as end points.

About the RIDER project

The Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat "coffee break," longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT] positron emission tomography [PET] CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):

• RIDER White Paper: Combined contracts report (Sept 2008) PDF

The long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the first RIDER white paper report (2006):

• RIDER White Paper: Executive Summary PDF
• RIDER White Paper: Editorial in Nature.com

The integration of diagnosis and therapy is an effective way to improve therapeutic effects for cancer patients, which has acquired widely attentions from researchers. Herein, a multifunctional drug-loaded nanosystem (F/A-PLGA@DOX/SPIO) has been designed and synthesized to reduce the side effects of traditional chemotherapy drugs and realize simultaneous tumor diagnosis and treatment. The surface modification of folic acid (FA) and activatable cell-penetrating peptide (ACPP) endows the nanosystem with excellent cancer targeting capabilities, thus reducing toxicity to normal organs. Besides, the F/A-PLGA@DOX/SPIO nanosystem can serve as an excellent magnetic resonance imaging (MRI) T2-negative contrast agent. More importantly, according to in vitro experiments, the F/A-PLGA@DOX/SPIO nanosystem can promote the overproduction of reactive oxygen species (ROS) within A549 lung cancer cells, inducing cell apoptosis, greatly enhancing the antineoplastic effect. Furthermore, with the help of MRI technology, the targeting imaging of the F/A-PLGA@DOX/SPIO nanosystem within tumors and the dynamic monitoring of medicine efficacy can be realized. Therefore, this study provided a multifunctional drug-loaded F/A-PLGA@DOX/SPIO targeted nanosystem for magnetic resonance molecular imaging-guided theranostics, which has excellent potential for the application in tumor diagnosis and therapy.

The Computer-Aided Detection (CAD) system market is experiencing robust growth, projected to reach $1774.2 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 8.4% from 2025 to 2033. This expansion is driven by several key factors. The rising prevalence of cancer, particularly breast, lung, colorectal, prostate, and liver cancers, fuels the demand for accurate and efficient diagnostic tools. CAD systems significantly enhance the speed and accuracy of radiologist interpretations, reducing human error and improving diagnostic outcomes. Technological advancements, including the integration of artificial intelligence (AI) and machine learning (ML) algorithms, are further refining CAD's capabilities, leading to improved sensitivity and specificity in detecting cancerous lesions. Furthermore, the increasing adoption of advanced imaging modalities like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), which benefit significantly from CAD assistance, contributes to market growth. The market is segmented by application (various cancer types) and imaging modality (X-ray, CT, Ultrasound, MRI, Nuclear Medicine), offering diverse opportunities for vendors. Geographic expansion, particularly in developing economies with growing healthcare infrastructure, presents significant growth potential. However, certain challenges hinder market expansion. High initial investment costs associated with purchasing and implementing CAD systems can be a barrier, particularly for smaller healthcare facilities. The need for skilled professionals to operate and interpret CAD results also presents a hurdle. Regulatory hurdles related to the approval and adoption of new technologies and concerns about data privacy and security are also factors influencing market growth. Despite these restraints, the overall market outlook remains positive, driven by the compelling clinical benefits of CAD systems and the continued advancements in imaging technology and AI. The competition among established players like Hologic, Siemens Healthcare, and Philips Healthcare, along with emerging technology companies, is intensifying, leading to innovation and market penetration. This competitive landscape, coupled with increasing healthcare expenditure globally, ensures continued market growth.