The MMOTU dataset consists of ovarian ultrasound images collected from Beijing Shijitan Hospital, Capital Medical University. The dataset is divided into two subsets: OTU 2D and OTU CEUS. The OTU 2D subset contains ultrasound images.The OTU CEUS subset consists of 170 images extracted from CEUS sequences.The MMOTU ovarian tumor ultrasound dataset used in the paper titled "PMFFNet: A hybrid network based on feature pyramid for ovarian tumor segmentation" is stored here. If needed, you can download and access it yourself. The dataset we employed in our study is sourced from the MMOTU image dataset, which comprises ovarian ultrasound images collected from Beijing Shijitan Hospital, Capital Medical University.If you would like to access the original MMOTU dataset, please click on the following link: https://drive.google.com/drive/folders/1c5n0fVKrM9-SZE1kacTXPt1pt844iAs1

• Wang et al. Weakly Supervised Deep Learning for Prediction of Treatment Effectiveness on Ovarian Cancer from Histopathology Images. Computerized Medical Imaging and Graphics. https://doi.org/10.1016/j.compmedimag.2022.102093
• Wang, CW., Chang, CC., Khalil, M.A. et al. Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer. Sci Data 9, 25 (2022). https://doi.org/10.1038/s41597-022-01127-6

The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the TCGA Ovarian Phenotype Research Group.

This dataset contains key characteristics about the data described in the Data Descriptor Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer.. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

This dataset provides detailed imaging data from various co-culture assays of ovarian cancer and fibroblast cell lines, treated with a wide range of drugs. The structured organization and comprehensive naming conventions allow for easy navigation and analysis of the data. The images are treated with 528 drugs from FIMM Oncology Library to study the drug effect on cancer cell morphology in presence of fibroblasts.

The dataset comprises images from 2D coculture high-content screening data in .tiff format. The images were acquired using the Opera Phenix at a 10x magnification. It includes a total of 245,760 raw images (including 4 field of views), each with a resolution of 1080x1080 pixels. For initial analysis, the images were read directly into CellProfiler, a software platform designed for high-throughput image analysis. To facilitate neural network processing, each image was converted into a NumPy array using Python 3 and the Python Imaging Library (PIL).

The data set is available for download in five separate ZIP archives, Kuramochi_BjhTERT.zip (93.68 GB), Kuramochi_WI38.zip (93.50 GB), MH_BjhTERT.zip (62.22 GB), OvCar3_BjhTERT.zip (84.98 GB), OvCar8_WI38.zip (91.89 GB).

For a description on the file structure, see associated documentation file Dataset_Description.pdf.

This dataset consists of 174 WSI ovary whole slide images (WSI): 158 malignant and 16 benign. Eight of the most common, histological definable tumour types were annotated: high grade serous carcinoma (HGSC), low grade serous carcinoma (LGSC), clear cell carcinoma (CC), endometrioid adenocarcinoma (EN), metastastic serous carcinoma (MS), metastatic other (MO), serous borderline tumor (SB) and mucinous borderline tumor (MB). Also normal ovarian tissue were annotated. 2402 separate annotations were made. For the benign structures only the epithelial structures, stroma and support tissue were annotated.

as technology in the field of artificial intelligence advances

IntroductionOvarian tumors are the most common diagnostic challenge for gynecologists and ultrasound examination has become the main technique for assessment of ovarian pathology and for preoperative distinction between malignant and benign ovarian tumors. However, ultrasonography is highly examiner-dependent and there may be an important variability between two different specialists when examining the same case. The objective of this work is the evaluation of different well-known Machine Learning (ML) systems to perform the automatic categorization of ovarian tumors from ultrasound images.MethodsWe have used a real patient database whose input features have been extracted from 348 images, from the IOTA tumor images database, holding together with the class labels of the images. For each patient case and ultrasound image, its input features have been previously extracted using Fourier descriptors computed on the Region Of Interest (ROI). Then, four ML techniques are considered for performing the classification stage: K-Nearest Neighbors (KNN), Linear Discriminant (LD), Support Vector Machine (SVM) and Extreme Learning Machine (ELM).ResultsAccording to our obtained results, the KNN classifier provides inaccurate predictions (less than 60% of accuracy) independently of the size of the local approximation, whereas the classifiers based on LD, SVM and ELM are robust in this biomedical classification (more than 85% of accuracy).ConclusionsML methods can be efficiently used for developing the classification stage in computer-aided diagnosis systems of ovarian tumor from ultrasound images. These approaches are able to provide automatic classification with a high rate of accuracy. Future work should aim at enhancing the classifier design using ensemble techniques. Another ongoing work is to exploit different kind of features extracted from ultrasound images.

This dataset contains ultrasound images of patients suffering from Polycystic Ovary Syndrome (PCOS) and normal patients. Each image in the dataset has been categorized into PCOS and Normal classes, which are annotations from specialist doctors.

In our study, we have generated proteomic and genomic (RNA sequencing and whole genome sequencing) profiles from high grade serous ovarian cancer (HGSOC) tumor biopsies. All biospecimens are formalin-fixed, parrafin-embedded (FFPE) tissues and annotated for patient sensitivity to platinum chemotherapy (refractory or sensitive). For all 174 tumors that were analyzed, we have H&E-stained and imaged the first and last sections (“bookend”) cut from each FFPE block to allow study of tumor pathology. These H&E pathology images are uploaded in this dataset. The 174 tumors represented 158 unique patients (imaging was performed on two FFPE blocks for a small subset of patients where additional tumor mass was required for proteomic analysis). The bookend FFPE slides were cut at 4 μm thickness using a microtome and mounted on glass slides (Leica Biosystems Cat# 3800040) for H&E staining. Digital images of the H&E slides were recorded using a ScanScope AT Slide Scanner (Leica Aperio Technologies, Vista, CA, USA) under 20X objective magnification (0.5 μm resolution). Images were analyzed by HALO Image Analysis Platform software (Indicta Labs, Albuquerque, New Mexico, USA).

The following clinical data are also provided for these subjects:

• Image file name (combination of Image Name and Image ID) and corresponding sample IDs
• Chemotherapy response status: (sensitive/refractory; refractory is defined in our study as clinical ovarian cancer that progresses while on platinum-based therapy or within 4 weeks)
• neo-adjuvant treatment (yes/no)
• Tumor location
• Tumor grade, stage, substage
• Patient age
• Patient ethnicity
• Patient race
• Age of sample

The goals of the study were to understand mechanisms of platinum resistance in epithelial ovarian cancers (EOCs) in order to: i) predict EOCs that will respond to DNA-damaging platinum therapy, and ii) identify potential new drug targets in resistant disease to point to desperately needed new therapeutic approaches. The ability to predict platinum-resistant/refractory disease would be clinically impactful by enabling the immediate triage of patients with refractory disease to clinical trials of experimental therapies, avoiding use of ineffective standard of care chemotherapy and helping to identify novel treatments. In this study, we generated proteomic and genomic (RNA sequencing and whole genome sequencing) profiling datasets and H&E images from high grade serous ovarian cancer (HGSOC) tumor biopsies representing platinum-sensitive and platinum-refractory disease.

Ovarian Cancer 3 DATA6000

## Overview

Ovarian Cancer  3  DATA6000 is a dataset for semantic segmentation tasks - it contains Malignant Cells annotations for 462 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Ovarian carcinoma is the deadliest cancer of the female reproductive system. It is also a heterogeneous disease with five common histotypes: high-grade serous carcinoma (HGSC) accounts for 70% of cases, clear cell ovarian carcinoma (CCOC) for 12%, endometrioid (ENOC) for 11%, low-grade serous (LGSC) for 4%, and mucinous carcinoma (MUC) for 3%. They differ in their cellular morphologies, etiologies, molecular, genetic, and clinical characteristics. Histotype-based treatment is becoming increasingly prevalent with the introduction of PARP inhibitor therapy for patients with HGSC. Ovarian cancer histotype classification by pathologists is associated with challenges in diagnostic reproducibility and interobserver disagreement. Initial diagnosis is performed through histological assessment of hematoxylin & eosin (H&E)-stained sections, but studies have shown that for pathologists without gynecologic pathology-specific training, the interobserver agreement is only moderate. Furthermore, the number of pathologists trained has not kept up with the increasing volume of cancer diagnoses. OCEAN is a scientific competition for developing an artificial intelligence (AI)-based software package for histopathology images of ovarian cancers. Our challenge comprises digitalized samples from 25 centers, with each image falling into one of three categories: normal, an outlier, and one of the five histotypes of ovarian cancer. Participants are asked to develop deep learning methodologies for classifying ovarian cancer histotypes and identifying outliers. Additionally, variations between slide scanners, different tissue processing and staining protocols across various pathology labs, and inter-patient variability can lead to inconsistent color appearances in histopathology sections; therefore, the generalizability of the developed software is a key aspect of the competition that participants need to take into consideration.

Objectives: To establish and validate a nomogram integrating radiomics signatures from ultrasound and clinical factors to discriminate between benign, borderline, and malignant serous ovarian tumors.Materials and methods: In this study, a total of 279 pathology-confirmed serous ovarian tumors collected from 265 patients between March 2013 and December 2016 were used. The training cohort was generated by randomly selecting 70% of each of the three types (benign, borderline, and malignant) of tumors, while the remaining 30% was included in the validation cohort. From the transabdominal ultrasound scanning of ovarian tumors, the radiomics features were extracted, and a score was calculated. The ability of radiomics to differentiate between the grades of ovarian tumors was tested by comparing benign vs borderline and malignant (task 1) and borderline vs malignant (task 2). These results were compared with the diagnostic performance and subjective assessment by junior and senior sonographers. Finally, a clinical-feature alone model and a combined clinical-radiomics (CCR) model were built using predictive nomograms for the two tasks. Receiver operating characteristic (ROC) analysis, calibration curve, and decision curve analysis (DCA) were performed to evaluate the model performance.Results: The US-based radiomics models performed satisfactorily in both the tasks, showing especially higher accuracy in the second task by successfully discriminating borderline and malignant ovarian serous tumors compared to the evaluations by senior sonographers (AUC = 0.789 for seniors and 0.877 for radiomics models in task one; AUC = 0.612 for senior and 0.839 for radiomics model in task 2). We showed that the CCR model, comprising CA125 level, lesion location, ascites, and radiomics signatures, performed the best (AUC = 0.937, 95%CI 0.905–0.969 in task 1, AUC = 0.924, 95%CI 0.876–0.971 in task 2) in the training as well as in the validation cohorts (AUC = 0.914, 95%CI 0.851–0.976 in task 1, AUC = 0.890, 95%CI 0.794–0.987 in task 2). The calibration curve and DCA analysis of the CCR model more accurately predicted the classification of the tumors than the clinical features alone.Conclusion: This study integrates novel radiomics signatures from ultrasound and clinical factors to create a nomogram to provide preoperative diagnostic information for differentiating between benign, borderline, and malignant ovarian serous tumors, thereby reducing unnecessary and risky biopsies and surgeries.

Ovarian Cancer Diagnostics and Therapeutics Market Outlook

The global ovarian cancer diagnostics and therapeutics market size was valued at approximately USD 2.6 billion in 2023 and is projected to reach USD 5.5 billion by 2032, registering a compound annual growth rate (CAGR) of 8.5% during the forecast period. The growth in this market is primarily driven by advancements in medical technology, increased awareness about cancer diagnosis and treatment options, and the rising prevalence of ovarian cancer worldwide. With improved diagnostic methodologies and an expanding portfolio of therapeutic options, the market is poised for significant progress over the next decade.

One of the primary growth factors for the ovarian cancer diagnostics and therapeutics market is the increasing global incidence of ovarian cancer. Ovarian cancer is one of the leading causes of cancer deaths among women, and its growing prevalence is a critical factor necessitating the development and implementation of advanced diagnostic and therapeutic solutions. The aging population, especially in developed regions, is also contributing to the rising incidence rates, as ovarian cancer risk significantly increases with age. Moreover, lifestyle changes and genetic predispositions are further exacerbating the potential for increased cases, thereby fueling market demand for effective diagnostic tools and therapeutic options.

Another significant growth factor is the technological advancements in the field of cancer diagnostics and treatment. Breakthroughs in imaging technologies, molecular diagnostics, and personalized medicine have revolutionized how ovarian cancer is detected and treated. Innovations such as next-generation sequencing and liquid biopsies are transforming the diagnostic landscape by enabling early detection and personalized treatment regimens. Additionally, the development and approval of new drugs and treatment modalities, including targeted therapy and immunotherapy, are enhancing treatment efficacy and patient outcomes, thus propelling market growth.

Government initiatives and funding for cancer research and treatment also play a crucial role in the growth of the ovarian cancer diagnostics and therapeutics market. Increased government spending on healthcare infrastructure, coupled with supportive policies to facilitate cancer research, is encouraging the development of new diagnostic and therapeutic solutions. Public awareness campaigns and educational programs about the importance of early detection and treatment of ovarian cancer are also contributing to the growth of the market by increasing patient awareness and encouraging proactive healthcare measures.

Regionally, North America holds a dominant position in the ovarian cancer diagnostics and therapeutics market, owing to the presence of advanced healthcare infrastructure, high healthcare expenditure, and a strong focus on research and development. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing healthcare investments, improving healthcare infrastructure, and a rising patient pool due to the increasing prevalence of ovarian cancer. The rapid economic growth in countries like China and India, coupled with growing awareness and availability of advanced healthcare solutions, are bolstering the market prospects in this region.

Product Type Analysis

The ovarian cancer diagnostics and therapeutics market is segmented into two primary product types: diagnostics and therapeutics. The diagnostics segment includes various methods used for early detection and confirmation of ovarian cancer, such as imaging tests, blood tests, and biopsies. Imaging tests, including ultrasound and CT scans, are essential tools in diagnosing ovarian cancer as they provide detailed images of the ovaries and surrounding areas, helping detect any abnormal growths. Blood tests, particularly those measuring cancer antigens like CA-125, are widely utilized as they offer non-invasive and cost-effective means of preliminary cancer detection. Additionally, biopsy procedures, though invasive, provide definitive diagnosis by allowing pathological examination of ovarian tissue.

The therapeutics segment encompasses several treatment modalities used to manage and treat ovarian cancer, including chemotherapy, targeted therapy, immunotherapy, and hormonal therapy. Chemotherapy remains the cornerstone of ovarian cancer treatment, often administered post-surgery to eradicate any residual cancer cells. Despite its effectiveness, chemotherapy is associated with significant side effects, prompting

Ovarian Cancer

## Overview

Ovarian Cancer is a dataset for object detection tasks - it contains Malignant Cells annotations for 750 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Contents:

This dataset contains the macroscopic and histological images of the ovaries of 202 Megrim (female, Lepidorhombus whiffiagonis, Walbaum, 1792) collected from the ICES Celtic Seas, south Greater North sea or Bay of Biscay Ecoregions (Eco) in November 2019 (n=25; Eco=7h & 7j), November 2020 (n=14, Eco=7h & 7j), December 2020 (n=1, Eco=7h), May 2021 (n=15, Eco=7h & 7g), June 2021 (n=15, Eco=7h), July 2021 (n=15, Eco=7h & 7e), October 2021 (n=15, Eco=7g & 7f), October 2021 (n=6, Eco=8a & 8b & 8c), November 2021 (n=6, Eco=8a & 8b), November 2021 (n=15, Eco=7j), December 2021 (n=15, Eco=7e & 7g), January 2022 (n=15, Eco=7f), February 2022 (n=15, Eco=7g), March 2022 (n=15, Eco=7g) and May 2022 (n=15, Eco=7g).

Images:

• Macroscopic_pictures.zip: archive in zip format of 549 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 202 female megrim dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder et al., 2022) with :
  • one picture of the entire fish with its abdominal cavity open with the ovaries in view
  • one picture of the whole fish with the ovaries outside of the abdominal cavity
  • one picture of the ovaries
  • the name of the picture is the same as the fish's ID number.
• Histology_slides.zip : archive in zip format containing the ovarian histological slides digitized using an Aperio CS (Scan Scope Console software, v.10.2.0.2352), x20 lens. The whole slide images (.svs) are of the 461 histological slides acquired during this study.

Data:

• Readings.zip : archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available.
  • Calibration : Reading results of 3 different agents, with the first and last readings, as well as the QuPath scripts used.
  • Homogeneity : Reading results for 102 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 102 slides belong to 17 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.
  • Total : Reading results for 202 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.
• Macro_WHI_read_me.txt : a text file (.txt) listing the acronyms used in the Macro_WHI.xlsx file, as well as their meaning.
• Macro_WHI.xlsx : Excel file (.xlsx) containing measurements of macroscopic parameters for all 202 fish sampled during this study. The information contained in this table is as follows:
  • Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (Macroscopic_pictures_Data)
  • ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27
  • ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27
  • Date: date the fish was caught (dd/mm/yyyy)
  • Total_fish_length: total length of the fish (cm)
  • Ungutted_fish_weight: total weight of the fish (g)
  • Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode et al., 2022) software used by IFREMER
  • Parasite: presence (Y) or absence (N) of parasite in or on the fish
  • age: age (in years) of the fish after analysis of the fish's otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis
  • Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish's gonad with the naked eye, following the WKASMSF (ICES, 2018) scale
  • Liver_weight: liver weight (g)
  • Droite_gonad_weight : gonad weight (g) of right ovary
  • Gauche_gonad_weight : gonad weight (g) of left ovary
  • Sections: number of cross sections sampled for the individual
• Stereo_WHI_read_me.txt : a text file (.txt) listing the acronyms used in the Stereo_WHI.csv file, as well as their meaning.
• Stereo_WHI.csv : a text data file (.csv) of the stereology count results of 287 slides read during this study. Among these slides, 102 were read to test the homogeneity distribution of different cell types found throughout each ovary (17 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see Calibration folder for reading results of the 3 agents). Finally, 202 median histological ovarian slides were read. The information contained in this table is as follows:
  • cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin et al. 2023)
  • idpt: identification number of the sampling point
  • id: unique complex identification number of the sampling point generated by combining the x and y coordinates
  • x: x coordinate of the sampling point
  • y: y coordinate of the sampling point
  • reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase
  • slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with Fish_id

Contact :

This dataset was established under the MATO (MATurité Objectif des poissons par l'histologie quantitative) project, during the PhD of Carine Sauger (October 2021-2023), financed by France Fillière Pêche (FFP/2020/AM/MF/109), under the supervision of IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) and BOREA (Biologie des Organismes et Ecosystèmes Aquatiques), and with the collaboration of a research facility from the University of Caen-Normandie : CMABIO3 (Centre de Microscopie Appliquée à la Biologie). For any enquiries, please contact: carine.sauger@gmail.com or laurent.dubroca@ifremer.fr

data set created from UBC Ovarian Cancer Subtype Classification and Outlier Detection (UBC-OCEAN) https://www.kaggle.com/competitions/UBC-OCEAN

and supplement data UBC Ovarian Cancer Competition Supplemental Masks https://www.kaggle.com/datasets/sohier/ubc-ovarian-cancer-competition-supplemental-masks/

This study was a multi-reader, multi-institutional, IRB-approved retrospective analysis of 93 HGSOC patients with abdominal and pelvic CT scans prior to primary debulking that were available through The Cancer Imaging Archive (TCIA). Eight radiologists from The Cancer Genome Atlas-Ovarian Cancer (TCGA-OV) Imaging Research Group developed and subsequently independently recorded the following CT features in each patient: primary ovarian mass(es) characteristics (if present), presence and distribution of peritoneal tumor spread, lymphadenopathy, and distant metastases. Inter-observer agreement for the CT features was assessed, as were associations of these features with time-to-disease progression (TTP) and CLOVAR subtypes and abilities of combinations of these features to predict TTP and CLOVAR subtypes. Results of analyzing this data are published in a manuscript titled Radiogenomics of High-Grade Serous Ovarian Cancer: Multi- Reader Multi-Institutional Study from The Cancer Genome Atlas-Ovarian Cancer (TCGA-OV) Imaging Research Group.

Ovarian Cancer Diagnostics Market Outlook

The global ovarian cancer diagnostics market size was valued at USD 1.2 billion in 2023 and is expected to reach USD 2.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3% during the forecast period. This growth is primarily driven by increasing prevalence of ovarian cancer, advancements in diagnostic technologies, and rising awareness about early diagnosis and treatment.

One of the major growth factors in the ovarian cancer diagnostics market is the rising incidence of ovarian cancer worldwide. According to the World Health Organization (WHO), ovarian cancer is among the top ten cancers affecting women globally. The need for early diagnosis is critical since early-stage ovarian cancer often presents with non-specific symptoms, leading to late-stage diagnoses, which complicates treatment. As awareness campaigns and screening programs become more prevalent, the demand for advanced diagnostic tools has surged, contributing significantly to the market's growth.

Technological advancements in diagnostics are also playing a pivotal role in the market's expansion. The development of novel biomarkers, enhanced imaging techniques, and the integration of artificial intelligence (AI) in diagnostic procedures have revolutionized ovarian cancer detection. Innovations such as liquid biopsy and next-generation sequencing (NGS) offer more accurate and less invasive diagnostic options. These advancements not only improve the diagnostic accuracy but also reduce the time and cost associated with traditional diagnostic methods, thus driving market growth.

Additionally, government and private sector investments in healthcare infrastructure and cancer research are further propelling the ovarian cancer diagnostics market. Substantial funding and grants for cancer research have led to significant breakthroughs in diagnostic technologies. Partnerships between research institutions and diagnostic companies are also fostering innovation and the development of new diagnostic tests. This collaborative ecosystem is essential for translating research findings into clinically viable diagnostic tools, thereby supporting market growth.

From a regional perspective, North America is expected to dominate the ovarian cancer diagnostics market during the forecast period. The region's robust healthcare infrastructure, high healthcare expenditure, and early adoption of advanced diagnostic technologies contribute to its leading position. Furthermore, strong governmental support and the presence of key market players in the region are boosting market growth. However, the Asia Pacific region is anticipated to witness the highest growth rate due to increasing healthcare awareness, rising incidence of ovarian cancer, and improving healthcare facilities.

Test Type Analysis

The ovarian cancer diagnostics market is segmented by test type into Imaging Tests, Blood Tests, Biopsy, Genetic Testing, and Others. Imaging tests, which include ultrasounds, CT scans, and MRIs, play a crucial role in detecting ovarian cancer. These tests provide detailed images of the ovaries and surrounding tissues, helping in the identification of tumors. The advancements in imaging technologies, such as higher resolution and 3D imaging, have significantly improved the accuracy of these tests. Moreover, the integration of AI in imaging analysis is enhancing diagnostic precision, thereby driving the growth of this segment.

Blood tests are another critical component of ovarian cancer diagnostics. These tests often measure the levels of specific biomarkers, such as CA-125, which can indicate the presence of ovarian cancer. The development of new biomarkers and assays has expanded the scope of blood tests, making them more reliable and comprehensive. Additionally, blood tests offer a less invasive diagnostic option compared to biopsies, making them a preferred choice for initial screening and monitoring disease progression. The increasing adoption of blood tests in routine check-ups and their role in early diagnosis are key factors driving their market growth.

Biopsy remains the gold standard for diagnosing ovarian cancer. This procedure involves the removal of tissue samples from the ovaries for microscopic examination. Advances in biopsy techniques, including image-guided biopsies and minimally invasive procedures, have improved the accuracy and safety of this diagnostic method. Despite being invasive, biopsies provide definitive confirmation of cancer, which is crucial for planning appropriate treatment strategies. The

This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute Imaging Data Commons (IDC) [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here. You can use the manifests included in this Zenodo record to download the content of the collection following the Download instructions below.

Collection description

Digital pathology images, augmented with the metadata describing their content, were converted into DICOM Whole Slide Microscopy (SM) representation [2,3] using custom open source scripts and tools as described in [4].

Files included

A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, collection_id-idc_v8-aws.s5cmd corresponds to the contents of the collection_id collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.

For each of the collections, the following manifest files are provided:

1. : manifest of files available for download from public IDC Amazon Web Services buckets
2. : manifest of files available for download from public IDC Google Cloud Storage buckets
3. : Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids)

Note that manifest files that end in -aws.s5cmd reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.

Download instructions

Each of the manifests include instructions in the header on how to download the included files.

To download the files using .s5cmd manifests:

1. install idc-index package: pip install --upgrade idc-index
2. download the files referenced by manifests included in this dataset by passing the .s5cmd manifest file: idc download manifest.s5cmd

To download the files using .dcf manifest, see manifest header.

Acknowledgments

Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.

References

[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023).

[2] National Electrical Manufacturers Association (NEMA). DICOM PS3.3 - Information Object Definitions: A.32.8 VL Whole Slide Microscopy Image IOD. at <https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8>

[3] Herrmann, M. D., Clunie, D. A., Fedorov, A., Doyle, S. W., Pieper, S., Klepeis, V., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., Kikinis, R., Kotecha, G. K., Hwang, D. H., Andriole, K. P., John Lafrate, A., Brink, J. A., Boland, G. W., Dreyer, K. J., Michalski, M., Golden, J. A., Louis, D. N. & Lennerz, J. K. Implementing the DICOM standard for digital pathology. J. Pathol. Inform. 9, 37 (2018).

[4] Clunie, D., Fedorov, A. & Herrmann, M. D. ImagingDataCommons/idc-wsi-conversion: Initial release. (Zenodo, 2023). doi:10.5281/ZENODO.8240154