PROSTATEx has been superseded by PI-CAI:

Accessing the PROSTATEx Challenge Data Sets

The PROSTATEx Challenge ("SPIE-AAPM-NCI Prostate MR Classification Challenge”) focused on quantitative image analysis methods for the diagnostic classification of clinically significant prostate cancers and was held in conjunction with the 2017 SPIE Medical Imaging Symposium. PROSTATEx ran from November 21, 2016 to January 15, 2017, though a "live" version has also been established at https://prostatex.grand-challenge.org which serves as an ongoing way for researchers to benchmark their performance for this task.

The PROSTATEx-2 Challenge ("SPIE-AAPM-NCI Prostate MR Gleason Grade Group Challenge" ) ran from May 15, 2017 to June 23, 2017 and was focused on the development of quantitative multi-parametric MRI biomarkers for the determination of Gleason Grade Group in prostate cancer. It was held in conjunction with the 2017 AAPM Annual Meeting (see http://www.aapm.org/GrandChallenge/PROSTATEx-2).

Supplemental data and instructions specific to both challenges are in the Detailed Description section below.

This collection is a retrospective set of prostate MR studies. All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE), and diffusion-weighted (DW) imaging. The images were acquired on two different types of Siemens 3T MR scanners, the MAGNETOM Trio and Skyra. T2-weighted images were acquired using a turbo spin echo sequence and had a resolution of around 0.5 mm in plane and a slice thickness of 3.6 mm. The DCE time series was acquired using a 3-D turbo flash gradient echo sequence with a resolution of around 1.5 mm in-plane, a slice thickness of 4 mm and a temporal resolution of 3.5 s. The proton density weighted image was acquired prior to the DCE time series using the same sequence with different echo and repetition times and a different flip angle. Finally, the DWI series were acquired with a single-shot echo planar imaging sequence with a resolution of 2 mm in-plane and 3.6 mm slice thickness and with diffusion-encoding gradients in three directions. Three b-values were acquired (50, 400, and 800), and subsequently, the ADC map was calculated by the scanner software. All images were acquired without an endorectal coil.

15 patients underwent a 60-minute dynamic 68Ga-PSMA-11 PET scan (patient 101P had its scan interrupted at 40 min) in list-mode with the field of view over the pelvic area. PET data were reconstructed with a 3D-Ordered-Subsets Expectation Maximization (OSEM) algorithm (3 iteration, 21 subsets, matrix 256x256, 4 mm Gaussian filter) and corrected for decay, scatter and attenuation using Dixon-based MR sequences. The list mode data were reconstructed into 28 frames (10 x 30 s, 5 x 60 s, 5 x 120 s, and 8 x 300 s).Volumes of interest (VOIs) were outlined on the PET images to derive time-activity curves (TACs) in unit Bq/mL. Tumor lesion TACs were derived from VOIs using isocontour threshold of 40% of maximum SUV on late PET images (last 15 min of scan) with the location confirmed on the T2-weighted MR images. Spherical VOIs of 1 mL were outlined on normal prostate and gluteus muscle to derive normal tissue TACs.Arterial blood activity was measured by continuous blood sampling from the radial artery during the first 10 min using an automatic blood sampling device with 1 s temporal resolution. Manual arterial blood samples at 6 time points (approximately 3, 7, 15, 25, 40 and 60 min post-injection). The manual blood samples were immediately put on ice and centrifuged to separate plasma. Whole blood and plasma-activities were measured in a gamma counter. The arterial input function was generated from the blood sampler curve corrected for decay, background and dispersion (15 s), merged with the decay-corrected manual whole blood samples to get a 60-min AIF. This whole-blood AIF was converted into a plasma AIF using the average plasma-to-blood activity ratios from the manual samples for each subject.IDIF were generated by extracting the median PET activity within a vessel mask at each timeframe. The vessel mask was defined by segmentation of both external iliac arteries clearly visible on Dixon MR registered to PET. The IDIF were corrected for spillover of PET activity from surrounding tissue to the arteries and partial volume errors using the approach described in Croteau et al 2010. The IDIF were converted to plasma curves using the average plasma-to-blood ratio determined from the arterial blood sampling.Included in the data set:- 1 excel file with patient data- 15 text files with blood data xxx_Blood: c_blo: whole-blood TAC, c_inp: plasma TAC (arterial input function)- 12 text files with image-derived blood data xxx_IDIF (patients 101, 152 and 207 excluded): c_blo: whole-blood IDIF TAC, c_inp: plasma IDIF TAC - 14 text files with tissue TACs xxx_TAC (subject 222 did not present any PSMA PET uptake and was excluded from lesion-based analysis): midtime and frame duration in min, uptake data in Bq/mL.

Purpose: This observational study investigates the influence of interfractional motion on clinical target volume (CTV) coverage, planning target volume (PTV) margins, and rectum tissue sparing in carbon ion radiation therapy (CIRT). It reports dose coverage to target structures and organs at risk in the presence of interfractional motion, investigates rectal tissue sparing, and provides recommendations for further lowering the rate of toxicity. We also propose probabilistic DVH for consideration in treatment planning to represent probable dose to the clinic’s patient population.

Methods: At Gunma University Hospital intensity-modulated x-ray therapy (IMXT, aka IMRT) prostate cancer patients are positioned on a table which is shifted twice based on cone-beam computed tomography (CBCT) to align bones and then align prostate tissue to isocenter. These shifts thereby contain interfractional motion. 1306 such tableshifts from 85 patients were collected. Normal probability distributions we...

Genome-wide association studies (GWAS) have revolutionized the field of cancer genetics, but the causal links between increased genetic risk and onset/progression of disease processes remain to be identified. Here we report the first step in such an endeavor for prostate cancer. We provide a comprehensive annotation of the 77 known risk loci, based upon highly correlated variants in biologically relevant chromatin annotations- we identified 727 such potentially functional SNPs. We also provide a detailed account of possible protein disruption, microRNA target sequence disruption and regulatory response element disruption of all correlated SNPs at r^2≥0.5. Greater than 88% of the 727 SNPs fall within putative enhancers, many of which alter critical residues in the response elements of transcription factors known to be involved in prostate biology. We define as risk enhancers those regions with enhancer chromatin biofeatures in prostate-derived cell lines with prostate-cancer correlated SNPs. To aid in the identification of these enhancers, we performed genomewide ChIP-seq for H3K27-acetylation, a mark of actively engaged enhancer regions, as well as the transcription factor TCF7L2. We analyzed in depth three variants in risk enhancers, two of which show significantly altered androgen sensitivity in LNCaP cells. This includes rs4907792, that is in linkage disequilibrium (r^2=0.91) with an eQTL for NUDT11 (on the X chromosome) in prostate tissue, and rs10486567, the index SNP in intron 3 of the JAZF1 gene on chromosome 7. Rs4907792 is within a critical residue of a strong consensus androgen response element that is interrupted in the protective allele, resulting in a 56% decrease in its androgen sensitivity, whereas rs10486567 affects both NKX3-1 and FOXA-AR motifs where the risk allele results in a 39% increase in basal activity and a 28% fold-increase in androgen stimulated enhancer activity. Identification of such enhancer variants and their potential target genes represents a preliminary step in connecting risk to disease process. ChIP-seq analysis of H3K27Ac in LNCaP charcoal-stripped serum, H3K27Ac in LNCaP charcoal-stripped serum +DHT, TCF7L2 in LNCaP

RATIONALE: Radiation therapy uses high-energy x-rays to damage tumor cells. Androgens can stimulate the growth of prostate cancer cells. Hormone therapy using bicalutamide may fight prostate cancer by reducing the production of androgens. It is not yet known if radiation therapy is more effective with or without bicalutamide for prostate cancer. PURPOSE: Randomized phase III trial to compare the effectiveness of radiation therapy with or without bicalutamide in treating patients who have stage II or stage III prostate cancer and elevated prostate-specific antigen (PSA) levels following radical prostatectomy.

Prostate cancer T1- and T2-weighted magnetic resonance images (MRIs) were acquired on a 1.5 T Philips Achieva by combined surface and endorectal coil, including dynamic contrast-enhanced images obtained prior to, during and after I.V. administration of 0.1 mmol/kg body weight of Gadolinium-DTPA (pentetic acid). Corresponding clinical metadata (XLS format) and 3D segmentation files (NRRD format) are offered as a supplement to this image collection. The XLS file contains pathology biopsy and excised gland tissue reports and the MRI radiology report for most subjects.

The Multi-component NRRD Segmentations allow visualization and downstream analysis in 3D Slicer of the following prostate components: prostate gland boundary; internal capsule; central gland, peripheral zone; seminal vesicles; urethra; cancer – dominant nodule; neurovascular bundle; penile bulb; ejaculatory duct; veru-montanum; and rectum. See our tutorial on Using 3D Slicer with the Prostate-Diagnosis data if you are not familiar with using this kind of data.

The Seminal vesicles (SV) and neurovascular bundle (NVB) Segmentations delineate the neurovascular bundle and seminal vessicles as MHA files. These were provided as part of a planned challenge competition that did not materialize.

The Third Party Analysis dataset mentioned beneath the Data Access table was added later as part of the NCI-ISBI 2013 Challenge - Automated Segmentation of Prostate Structures. It includes segmentations for 30 Prostate-Diagnosis subjects in NRRD format which mark the boundaries of the central gland and peripheral zone were also provided

NGS-ProToCol RNA-seq dataset contains 41x normal adjacent prostate and 51x prostate cancer samples taken from fresh frozen radical prostatectomies, sequenced using random-hexamer priming. RNA-seq was performed on the Illumina HiSeq 2500 platform, 2 x 126 bp stranded paired-end reads at a depth of 70 mln reads.

Raw and adjusted mean most common lesion scores for the anterior, dorsal, lateral, and ventral prostate lobes1.

To test model sensitivity to overdiagnosis, in the bottom two tables a 20% overdiagnosis rate was applied. Because of imperfect screening sensitivity, the overdiagnosis rate in the simulated sample population is actually less than 20%. Unbiased RR = 1. a) Usual-care group sampled from entire enrollment period: Simulation results using smoked variable: Lognormal distribution for preclinical duration with no overdiagnosis and chest x-ray sensitivity of 66%. b) Intervention group sampled after procedural modification: Simulation results using smoked variable: Lognormal distribution for preclinical duration with no overdiagnosis and chest x-ray sensitivity of 66%. c) Usual-care group sampled from entire enrollment period: Simulation results using smoked variable: Lognormal distribution for preclinical duration with 20% overdiagnosis and chest x-ray sensitivity of 66%. d) Intervention group sampled after procedural modification: Simulation results using smoked variable: Lognormal distribution for preclinical duration with 20% overdiagnosis and chest x-ray sensitivity of 66%.

Polyploid Giant Cancer Cells (PGCC) are increasingly being studied for their role in cancer recurrence and mortality. PGCC arise from cancer cells in response to stress such as radiation, chemotherapy, or hypoxia. PGCC remain viable but do not divide by mitosis. However, upon cessation of stress, PGCC are capable of progeny formation via primitive, amitotic mechanisms akin to budding or bursting. In this study, the initial PGCC offspring are referred to as “early progeny”. As early progeny cells resume proliferation they generate “late progeny”. This study assessed the transcriptional changes that occur as parental cancer cells progress to PGCC in response to radiation stress, escape as early progeny, and resume proliferation. The experiment included four conditions. For each, 7 X 10^5 PPC1 cells were plated on day 1 in 100 mm plates. Condition 1 (UT) were simply harvested on day 4 with trypsinization, processed using a Qiagen RNAEasy kit, purity assessed with a Nanodrop, and packaged for delivery to Novogene for analysis. Condition 2 (Gi) were irradiated with 8 Gy on day 2 and on day 4 trypsinized, passed through a 20 micron PluriSelect cell filter, and the captured cells eluted. Condition 3 (ErD) were irradiated on day 2 and on day 8 trypsinized, passed through a 20 micron PluriSelect cell filter, and the flowthrough processed for analysis. Condition 4 (Late) were irradiated on day 2 and on day 20 trypsinized, passed through a 20 micron PluriSelect filter, and the flowthrough processed. The experiment was performed with 6 replicates, for a total of 24 samples.

The values are the mean total AR positive cells per tissue area (μm2) ± SEM in prostate epithelium, hyperplasia or tumor.

This dataset contains WGBS sequencing data of lymphnode metastasis samples from prostate cancer. Sequencing was performed on Illumina HiSeq X Ten. The sequencing was always paired.

The global prostate cancer drugs market is estimated to reach USD X million by 2033, growing at a CAGR of XX% during the forecast period 2025-2033. The market is driven by the increasing prevalence of prostate cancer, advancements in treatment options, and government initiatives to raise awareness and improve access to care. Increasing geriatric population, unhealthy lifestyle choices, and environmental factors also contribute to the market growth. The major players in the prostate cancer drugs market include AbbVie, Astellas Pharma, Astra Zeneca, Johnson & Johnson, Sanofi, GlaxoSmithKline, Merck Group, Novartis, Amgen, Bayer HealthCare, Ferring Pharmaceutical, Janssen Pharmaceuticals, Endo Pharmaceuticals, BMS, Takeda Pharmaceuticals, Northwest Biotherapeutics, Teva Pharmaceutical, Boehringer Ingelheim, Foresee Pharmaceuticals, Tokai Pharmaceuticals, and Spectrum Pharmaceuticals. These companies are actively involved in research and development of new and advanced treatment options, as well as expanding their geographical reach through strategic partnerships and acquisitions.

Background: Focused ultrasound (FUS) has become an important non-invasive therapy for prostate tumor ablation via thermal effects in the clinic. The cavitation effect induced by FUS is applied for histotripsy, support drug delivery, and the induction of blood vessel destruction for cancer therapy. Numerous studies report that cavitation-induced sonoporation could provoke multiple anti-proliferative effects on cancer cells. Therefore, cavitation alone or in combination with thermal treatment is of great interest but research in this field is inadequate.Methods: Human prostate cancer cells (LNCap and PC-3) were exposed to 40 s cavitation using a FUS system, followed by water bath hyperthermia (HT). The clonogenic assay, WST-1 assay, and Transwell® invasion assay, respectively, were used to assess cancer cell clonogenic survival, metabolic activity, and invasion potential. Fluorescence microscopy using propidium iodide (PI) as a probe of cell membrane integrity was used to identify sonoporation. The H2A.X assay and Nicoletti test were conducted in the mechanism investigation to detect DNA double-strand breaks (DSBs) and cell cycle arrest. Immunofluorescence microscopy and flow cytometry were performed to determine the distribution and expression of 5α-reductase (SRD5A).Results: Short FUS shots with cavitation (FUS-Cav) in combination with HT resulted in, respectively, a 2.2, 2.3, and 2.8-fold decrease (LNCap) and a 2.0, 1.5, and 1.6-fold decrease (PC-3) in the clonogenic survival, cell invasiveness and metabolic activity of prostate cancer cells when compared to HT alone. FUS-Cav immediately induced sonoporation in 61.7% of LNCap cells, and the combination treatment led to a 1.4 (LNCap) and 1.6-fold (PC-3) increase in the number of DSBs compared to HT alone. Meanwhile, the combination therapy resulted in 26.68% of LNCap and 31.70% of PC-3 with cell cycle arrest in the Sub-G1 phase and 35.37% of PC-3 with cell cycle arrest in the G2/M phase. Additionally, the treatment of FUS-Cav combined with HT block the androgen receptor (AR) signal pathway by reducing the relative Type I 5α-reductase (SRD5A1) level to 38.28 ± 3.76% in LNCap cells, and decreasing the relative Type III 5α-reductase 3 (SRD5A3) level to 22.87 ± 4.88% in PC-3 cells, in contrast, the relative SRD5A level in untreated groups was set to 100%.Conclusion: FUS-induced cavitation increases the effects of HT by interrupting cancer cell membranes, inducing the DSBs and cell cycle arrest, and blocking the AR signal pathway of the prostate cancer cells, with the potential to be a promising adjuvant therapy in prostate cancer treatment.

Summary Dataset of imaging data related to the publication Raudenska, M., Kratochvilova, M., Vicar, T., Gumulec, J., Balvan, J., Polanska, H. Pribyl, J. & Masarik, M.:Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation. Scientific Reports 2019, 9, 1660 This dataset includes image data of atomic force microcopy (Young modulus) and confocal microscopy(staining of F-actin and β-tubulin) of prostate cell lines PNT1A, 22Rv1, and PC-3. Materials and Methods Cells, cell culture conditions Cells confluent up to 50–60% were washed with a FBS-free medium and treated with a fresh medium with FBS and required antineoplastic drug concentration (IC50 concentration for the particular cell line). The cells were treated with 93 µM (PC-3), 38 µM (PNT1A), and 24 µM (22Rv1) of cisplatin (Sigma-Aldrich, St. Louis, Missouri), respectively. IC50 concentrations used for treatment with docetaxel (Sigma-Aldrich, St. Louis, Missouri) were 200nM for PC-3, 70nM for PNT1A, and 150nM for 22Rv1. Long-term zinc (II) treatment of cell cultures Cells were cultivated in the constant presence of zinc(II) ions. Concentrations of zinc(II) sulphate in the medium were increased gradually by small changes of 25 or 50 µM. The cells were cultivated at each concentration no less than one week before harvesting and their viability was checked before adding more zinc. This process was used to select zinc resistant cells naturally and to ensure better accumulation of zinc within the cells (accumulation of zinc is usually poor during the short-term treatment of prostate cancer cells). Total time of the cultivation of cell lines in the zinc(II)-containing media exceeded one year. Resulting concentrations of zinc(II) in the media (IC50 for the particular cell line) were 50 µM for the PC-3 cell line, 150 µM for the PNT1A cell line, and 400 µM for the 22Rv1 cell line. The concentrations of zinc(II) in the media and FBS were taken into account. Actin and tubulin staining β-tubulin was labeled with anti- β tubulin antibody EPR1330 at a working dilution of 1/300. The secondary antibody used was Alexa Fluor® 555 donkey anti-rabbit (ab150074) at a dilution of 1/1000. Actin was labeled with Alexa Fluor™ 488 Phalloidin (A12379, Invitrogen); 1 unit per slide. For mounting Duolink® In Situ Mounting Medium with DAPI (DUO82040) was used. The cells were fixed in 3.7% paraformaldehyde and permeabilized using 0.1% Triton X-100. Confocal microscopy The microscopy of samples was performed at the Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic. Leica DM RXA microscope (equipped with DMSTC motorized stage, Piezzo z-movement, MicroMax CCD camera, CSU-10 confocal unit and 488, 562, and 714 nm laser diodes with AOTF) was used for acquiring detailed cell images (100× oil immersion Plan Fluotar lens, NA 1.3). Total 50 Z slices was captured with Z step size 0.3 μm. Atomic force microscopy We used the bioAFM microscope JPK NanoWizard 3 (JPK, Berlin, Germany) placed on the inverted optical microscope Olympus IX‑81 (Olympus, Tokyo, Japan) equipped with the fluorescence and confocal module, thus allowing a combined experiment (AFM‑optical combined images). The maximal scanning range of the AFM microscope in X‑Y‑Z range was 100‑100‑15 µm. The typical approach/retract settings were identical with a 15 μm extend/retract length, Setpoint value of 1 nN, a pixel rate of 2048 Hz and a speed of 30 µm/s. The system operated under closed-loop control. After reaching the selected contact force, the cantilever was retracted. The retraction length of 15 μm was sufficient to overcome any adhesion between the tip and the sample and to make sure that the cantilever had been completely retracted from the sample surface. Force‑distance (FD) curve was recorded at each point of the cantilever approach/retract movement. AFM measurements were obtained at 37°C (Petri dish heater, JPK) with force measurements recorded at a pulling speed of 30 µm/s (extension time 0.5 sec). The Young's modulus (E) was calculated by fitting the Hertzian‑Sneddon model on the FD curves measured as force maps (64x64 points) of the region containing either a single cell or multiple cells. JPK data evaluation software was used for the batch processing of measured data. The adjustment of the cantilever position above the sample was carried out under the microscope by controlling the position of the AFM‑head by motorized stage equipped with Petri dish heater (JPK) allowing precise positioning of the sample together with a constant elevated temperature of the sample for the whole period of the experiment. Soft uncoated AFM probes HYDRA-2R-100N (Applied NanoStructures, Mountain View, CA, USA), i.e. silicon nitride cantilevers with silicon tips are used for stiffness studies because they are maximally gentle to living cells (not causing mechanical stimulation). Moreover, as compared with coated cantilevers, these probe...

Each tissue image was provided in its native .ndpi format, which can be handled by the OpenSlide (www.openslide.org) software. The .ndpi TMA DISH image contains all 71 images, from which individual TMA cores can be extracted (PTEN_Hamamatsu.ndpi). The same applies to the .ndpi TMA H&E image (HE_Hamamatsu.ndpi). The .ndpi image for each of the two whole slide images (B12-7405_8.ndpi and B05-41379_10.ndpi) is in its original resolution and is not tiled into 2,000 x 2,000 pixels.

prostate and testis expressed opposite C1QTNF9B and MIPEP Predicted to be located in cytoplasm. Predicted to be located in cytoplasm. [provided by Alliance of Genome Resources, Feb 2025]

Binary semantic segmentation for detection of prostate adenocarcinoma using an ensemble with attention and residual U-Net architecturesAn accurate determination of the Gleason Score or Gleason Pattern (GP) is crucial in the diagnosis of prostate cancer (PCa) because it is one of the criterion used to guide treatment decisions for prognostic-risk groups. However, the manually designation of GP by a pathologist using a microscope is prone to error and subject to significant inter-observer variability. Deep learning has been used to automatically differentiate GP on digitized slides, aiding pathologists and reducing inter-observer variability, especially in the early GP of cancer. This paper presents a binary semantic segmentation for the GP of prostate adenocarcinoma. The segmentation separates benign and malignant tissues, with the malignant class consisting of adenocarcinoma GP3 and GP4 tissues annotated from 50 unique digitized whole slide images (WSIs) of prostate needle core biopsy specimens stained with hematoxylin and eosin. The pyramidal digitized WSIs were extracted into image patches with a size of 256 x 256 pixels at a magnification of 20X. An ensemble approach is proposed combining U-Net-based architectures, including traditional U-Net, attention-based U-Net, and residual attention-based U-Net. This work initially considers a PCa tissue analysis using a combination of attention gate units with residual convolution units. The performance evaluation revealed a mean Intersection-over-Union of 0.79 for the two classes, 0.88 for the benign class, and 0.70 for the malignant class. The proposed method was then used to produce pixel-level segmentation maps of PCa adenocarcinoma tissue slides in the testing set. We developed a screening tool to discriminate between benign and malignant prostate tissue in digitized images of needle biopsy samples using an AI approach. We aimed to identify malignant adenocarcinoma tissues from our own collected, annotated, and organized dataset. Our approach returned the performance which was accepted by the pathologists.

Radiotherapy of prostate cancer (PC) can lead to the acquisition of radioresistance through molecular mechanisms that involve, in part, cell adhesion-mediated signaling. To define these mechanisms, we employed a DU145 PC model to conduct a comparative mass spectrometry-based proteomic analysis of the purified integrin nexus, i.e., the cell-matrix junction where integrins bridge assembled extracellular matrix (matrisome components) to adhesion signaling complexes (adhesome components). When parental and radioresistant cells were compared, the expression of integrins was not changed, but cell radioresistance was associated with extensive matrix remodeling and changes in the complement of adhesion signaling proteins. Out of 72 proteins differentially expressed in the parental and radioresistant cells, four proteins were selected for functional validation based on their correlation with biochemical recurrence-free survival. Perlecan/heparan sulfate proteoglycan 2 (HSPG2) and lysyl-like oxidase-like 2 (LOXL2) were upregulated, while sushi repeat-containing protein X-linked (SRPX) and laminin subunit beta 3 (LAMB3) were downregulated in radioresistant DU145 cells. Knockdown of perlecan/HSPG2 sensitized radioresistant DU145 RR cells to irradiation while the sensitivity of DU145 parental cells did not change, indicating a potential role for perlecan/HSPG2 and its associated proteins in suppressing tumor radioresistance. Validation in androgen-sensitive parental and radioresistant LNCaP cells further supported perlecan/HSPG2 as a regulator of cell radiosensitivity. These findings extend our understanding of the interplay between extracellular matrix remodeling and PC radioresistance and signpost perlecan/HSPG2 as a potential therapeutic target and biomarker for PC.

The global Lymph Node Intelligent Analysis System market, currently valued at $281 million in 2025, is projected to experience steady growth, driven by a Compound Annual Growth Rate (CAGR) of 3% from 2025 to 2033. This growth is fueled by several key factors. The increasing prevalence of cancers like lung, breast, gastrointestinal, and prostate cancer, which necessitate accurate lymph node analysis, is a major driver. Advancements in medical imaging technologies, particularly CT and MRI, coupled with the development of sophisticated AI-powered image analysis software, are significantly enhancing the speed and accuracy of lymph node assessment. Furthermore, the growing adoption of minimally invasive surgical techniques, which require precise pre-operative planning facilitated by intelligent analysis systems, is further boosting market demand. The market is segmented by application (cancer type) and technology (imaging modality), with the CT-based segment currently dominating due to its wider availability and established role in cancer diagnosis. Key players like Cai-X, Definiens, Celsus, Paige AI, Shanghai Lianying Intelligent Medical Technology, and AthenaEyes are actively contributing to market innovation and expansion through continuous product development and strategic partnerships. Geographical distribution shows a concentration in North America and Europe, reflecting higher healthcare expenditure and advanced healthcare infrastructure in these regions. However, rapidly developing economies in Asia-Pacific are expected to witness significant growth in the coming years, driven by increasing healthcare investments and rising cancer incidence rates. The restraints to market growth include the high initial investment costs associated with acquiring and implementing these sophisticated systems, along with the need for specialized training for healthcare professionals to effectively utilize the technology. Regulatory hurdles and variations in reimbursement policies across different geographical regions can also pose challenges. Despite these challenges, the long-term outlook remains positive, with the market poised for substantial expansion driven by technological advancements, increasing awareness about the benefits of accurate lymph node assessment, and the rising global cancer burden. The ongoing research and development efforts focused on improving the accuracy, efficiency, and accessibility of lymph node intelligent analysis systems are expected to further fuel market growth throughout the forecast period.