Sample of labels associated to diagnosed_biopsies. Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
File of images with melanoma between 1 and 2 millimeter
File with melanome with missing value of Breslow
Sample of labels associated with diagnosed_conizations. Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
Sample of images of diagnosed_biopsies. Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
Metadata associated with diagnosed_conizations (21 slides). Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
Sample of metadata associated to diagnosed_biopsies. Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
Images of diagnosed_conizations (21 slides). Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
Images of undiagnosed_biopsies = 217 slides There are no labels for these images or corresponding annotations
Sample of images of undiagnosed biopsies. There are no labels for these images or corresponding annotations
File with melanomes with Breslow under 1 mm
File with melanome images with Breslow between 2 and 4 mm
Images annotated_biopsies (295 slides). Pathologists have labeled and annotated these images to point out regions that represent lesions. When working with the annotations, it's important to keep in mind the following points: - The annotated regions do not necessarily include all lesioned tissue in the slide. An unannotated region is not necessarily normal tissue. - The whole image class label and the annotation class label do not necessarily match. The annotated regions may be the image's labeled class or below. For instance, an image labeled as a class 2 lesion could have annotations representing class 0, 1, or 2. At least some of the annotated regions will represent the most severe/labeled class. All annotations on a slide with label 0 will be normal tissue. - The lesion may fall entirely within the square, or may extend beyond the annotation boundaries. - All annotations are a fixed size of 300x300 micrometers. As images have different resolutions in pixels/micrometer, annotations will have different dimensions in terms of pixels. - When using the geometries, it is important to know the origin of the coordinate system. Image processing software may assume the image origin is either the bottom left or the top left. The WKT shapes that we provide as annotations (geometry column in train_annotations.csv) are relative to the bottom left being the origin (0, 0).
## **1. Purpose of the database:** This database was collected in order to organize the [TissueNet Data Challenge](https://www.drivendata.org/competitions/67/competition-cervical-biopsy/). This dataset consists of high resolution images of microscopic slides created from cervical biopsies and surgical specimens. Additionally, the competitors were given slide metadata as well as annotations for the training set that outlined some (but not necessarily all) of the lesions present on a slide. The database shared on data.gouv.fr is a portion of the database used for the data challenge. This database contains the slides coming from the pathological centers that have agreed to share the data openly. This database includes 1272 microscopic slides of uterine cervical tissue from medical centers across France. The slides are distributed in the following datasets: diagnosed\_biopsies = 443 slides diagnosed\_conizations = 21 slides annotated\_biopsies = 295 slides undiagnosed\_biopsies = 217 slides undiagnosed\_conizations = 296 slides * **Diagnosed\_biopsies & diagnosed\_conizations:** Pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization (5th edition): 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level). * **Fully\_annotated\_biopsies:** Pathologists have labeled and annotated these images to point out regions that represent lesions. When working with the annotations, it's important to keep in mind the following points: \-- The annotated regions do not necessarily include all lesioned tissue in the slide. An unannotated region is not necessarily normal tissue. \-- The whole image class label and the annotation class label do not necessarily match. The annotated regions may be the image's labeled class or below. For instance, an image labeled as a class 2 lesion could have annotations representing class 0, 1, or 2. At least some of the annotated regions will represent the most severe/labeled class. All annotations on a slide with label 0 will be normal tissue. \-- The lesion may fall entirely within the square, or may extend beyond the annotation boundaries. \-- All annotations are a fixed size of 300x300 micrometers. As images have different resolutions in pixels/micrometer, annotations will have different dimensions in terms of pixels. \-- When using the geometries, it is important to know the origin of the coordinate system. Image processing software may assume the image origin is either the bottom left or the top left. The WKT shapes that we provide as annotations (geometry column in train\_annotations.csv) are relative to the bottom left being the origin (0, 0). * **Undiagnosed\_biopsies & undiagnosed\_conizations:** There are no labels for these images or corresponding annotations All images are standardized in pyramidal TIF format. These images are compressed using JPEG Q=75. The pyramidal TIF format maintains a sufficient level of detail for pathologists to perform diagnoses while enabling smaller file sizes and easier loading with actively developed Python libraries such as [PyVips](https://github.com/libvips/pyvips). ## **2. Context of creation of the database:** This database was created as part of the TissueNet Data Challenge. This challenge began in 2019 when the French Society of Pathology (SFP) and the Health Data Hub (HDH) decided to build a challenge using a data bank of whole slide images (WSIs). Nineteen public and private pathology departments across France contributed more than 5,000 WSIs as data for the challenge. These slides are often difficult for pathologists themselves to diagnose, and expert eyes may be required. All labeled images included in the challenge were reviewed twice by expert pathologists. The database shared on data.gouv.fr is a portion of the database used for the data challenge. This database contains the slides coming from the pathological centers that have agreed to share the data openly. This database includes 1272 microscopic slides of uterine cervical tissue from medical centers across France. ## **3. Target:** Data challenges are global competitions aimed at solving specific problems within a given time frame using highly anonymized data. Thus, these challenges are intended for data scientists (researcher, industrials, students etc.) from all around the world. The objective of the challenge was to classify each image according to the most severe category of epithelial lesion present in the sample. The classes are defined as follows: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) ## **4. Results obtained from the database:** In the TissueNet competition, participants were tasked with building machine learning models that could predict the most severe lesions in each digital biopsy slide. What's more, participants needed to submit code for executing their solution on test data in the cloud, ensuring that the model could run fast enough on this large scale data to be useful in practice. This setup rewards models that perform well on unseen images and brings these innovations one step closer to impact. Global performance of each algorithm was evaluated according to a custom metric devised by a panel of expert pathologists. The score for each prediction equals 1 minus the error, where the error weighting for misclassification has been set by an expert consensus within the scientific council as defined in the table below. The total error is the average error across all predictions. Note that the metric is symmetric, e.g., predicting class 3 when it is actually class 0 produces the same error as predicting class 0 when it is actually class 3. Error table of misclassification: ![Error table of misclassification:](https://gitlab.com/healthdatahub/open-data-images-data.gouv.fr/base-de-donnees-tissuenet/-/raw/main/table_1.png?ref_type=heads "Error table of misclassification:") The winning solutions used clever approaches to prioritize the parts of each slide to analyze further, and built computer vision pipelines to determine the most appropriate diagnosis for the selected tissue. Models were scored not just on their accuracy, but also on the impact of their errors (providing a large penalty for mistakes that have worse consequences in practice). The top-performing model achieved over 76% accuracy in predicting the exact severity label of each slide across 4 ranked classes, including 95% accuracy for the most severe class of cancerous tissue. In addition, the top 3 solutions achieved >98% on-or-adjacent accuracy, meaning they reduced the more costly misclassifications that erred by more than one class to less than 2% of the 1,500+ slide test set! ![](https://gitlab.com/healthdatahub/open-data-images-data.gouv.fr/base-de-donnees-tissuenet/-/raw/main/image_1.png?ref_type=heads) All prize-winning solutions are available under an open source license for ongoing use and learning. For more details : [winning models on GitHub](https://github.com/drivendataorg/tissuenet-cervical-biopsies) ## **5. Other informations:** Here are some resources you can use in order to work with the data : -[OpenSlide](https://openslide.org/api/python/) supports all native whole slide image formats, including: .mrxs (MIRAX) .svs (Aperio) .ndpi (Hamamatsu) -[PyVips](https://pypi.org/project/pyvips/) is a Python binding for [libvips](https://libvips.github.io/libvips/), a low-level library for working with large images. PyVips can be used to read and manipulate the pyramidal TIF formats. -[Cytomine](https://doc.cytomine.org/UsersV2) allows you to display and explore native whole slide images and pyramidal TIF formats in a web browser. It also supports adding annotations and [executing scripts](https://doc.cytomine.org/DataScientists) from inside Cytomine or from any computing server using the dedicated [Cytomine Python client](https://doc.cytomine.org/Python-client). Cytomine can be installed locally or on any Linux server. The Cytomine GitHub repository includes [examples of Python scripts](https://github.com/cytomine/Cytomine-python-client/tree/master/examples) demonstrating how to interact with your Cytomine instance, as well as examples of ready-to-use machine learning scripts (all S\_ prefixed repos, such as [S\_CellDetect\_Stardist\_HE\_ROI](https://github.com/cytomine/S_CellDetect_Stardist_HE_ROI)). Here are a few papers and tutorials that talk about machine learning with WSI that you may find helpful: * [Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge?](https://www.sciencedirect.com/science/article/pii/S215335392200743X?via%3Dihub#) * [Le premier data challenge organisé par la Société Française de Pathologie : une compétition internationale en 2020, un outil de recherche en intelligence artificielle pour l’avenir ?The first data challenge of the french society of pathology: An international competition in 2020, a research tool in A.I. for the future?](https://www.sciencedirect.com/science/article/abs/pii/S0242649821002467?via%3Dihub) * [Whole slide image preprocessing in Python](https://developer.ibm.com/articles/an-automatic-method-to-identify-tissues-from-big-whole-slide-images-pt1/) * [Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions - PubMed](https://arxiv.org/pdf/1905.13208.pdf) * [Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies](https://www.nature.com/articles/s41379-018-0073-z/figures/1) * [Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions](https://pubmed.ncbi.nlm.nih.gov/31397859/) * [Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens](https://www.nature.com/articles/s41598-020-66849-2) * [Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children](https://pubmed.ncbi.nlm.nih.gov/31199451/) ## **6. Licences:** Creative Commons Attribution ([CC BY 3.0](https://creativecommons.org/licenses/by/3.0/fr/deed.fr)) Licence Ouverte/Open Licence 2.0 ([Etalab 2.0](https://www.etalab.gouv.fr/wp-content/uploads/2017/04/ETALAB-Licence-Ouverte-v2.0.pdf)) ## **7. User form:** [USER FORM](https://docs.google.com/forms/d/e/1FAIpQLSdxXh01PyGL1n4i9qj90oTxcuiEu7RvWOHzbif6DtSlZI2_ow/viewform?usp=sf_link) The purpose of the user form is to track who (in terms of individuals and institutions) is using the data and potentially for what purposes. This form is not restrictive in the sense that access requests will never be denied. ## **7. Cite:** For any reuse of this database, use the DOI provided below: <https://doi.org/10.60597/eaqa-k904>
TissueNet: detect lesions in uterine cervix specimens - Open data set
Sample of metadata of undiagnosed_biopsies There are no labels for these images or corresponding annotations
This file is a pdf with 16 annotated slides in order to help you understand how pathologists analyse each slides.
Labels associated to diagnosed_biopsies (443 slides). Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
Images of undiagnosed conizations (296 slides). There are no labels for these images or corresponding annotations
Sample of images of undiagnosed conizations. undiagnosed_conizations_sample_images.zip.
Labels associated with diagnosed_conizations (21 slides). Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
Images of diagnosed_biopsies (443 slides). Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
## **1. Purpose of the database:** Melanoma is a cancer arising from melanocytes—the pigment-producing cells located primarily in the skin, but also, less commonly, in mucosal membranes. While most cases occur in the skin, mucosal melanomas represent a rarer but clinically significant subset. The pathogenesis of cutaneous melanoma is multifactorial, involving a combination of environmental factors — particularly ultraviolet (UV) radiation — host-related characteristics, such as the presence of atypical nevi, a high total nevus count, and skin phototype, as well as inherited genetic susceptibility. Although they represent only about 10% of all skin cancers, cutaneous melanoma are among the most aggressive forms of skin cancers, primarily due to their high metastatic potential, which critically worsens the prognosis. Metastatic dissemination occurs when malignant cells from the primary tumor invade surrounding tissues and establish secondary lesions in regional lymph nodes (referred to as loco-regional melanoma) or distant organs (distant metastases). Metastases are infrequently present at the time of initial diagnosis and generally emerge during disease monitoring or follow-up. Diagnosis of melanoma involves microscopic analysis of tumor tissue by a pathologist. By examining a stained histological slide, the pathologist confirms the disease diagnosis and assesses lesion severity based on prognostic factors such as the tumor thickness or the presence of ulceration. Combining this analysis with clinical prognostic factors (such as age, sex, and medical history) enables dermatologists to adapt treatment to the disease's severity. Patient survival in melanoma is highly dependent on the stage at diagnosis. In cases of primary cutaneous melanoma without metastatic spread, prognosis is strongly associated with tumor thickness. For example, early-stage melanomas measuring less than 1 mm in thickness have a 5-year survival rate exceeding 95%. In contrast, melanomas thicker than 4 mm are associated with a relapse risk of approximately 70 to 80% within five years, indicating a substantial risk of disease recurrence after initial treatment. In patients presenting with metastatic melanoma at diagnosis or experiencing relapse, treatment options may include surgical interventions — such as lymph node dissection or metastasectomy — as well as systemic therapies, including immunotherapy and targeted therapies. Although thin melanomas (<1 mm) are generally associated with favorable outcomes, they still account for a non-negligible and poorly understood proportion of recurrences and melanoma-related mortality. Similarly, intermediate-thickness melanomas (1–4 mm), which carry a higher risk of recurrence, currently lack reliable biomarkers or clinical predictors of metastatic progression. Adjuvant therapies are available to reduce the risk of relapse in selected high-risk, resectable melanomas. However, these treatments are associated with substantial financial costs and potentially severe toxicities. Therefore, there is a pressing need to identify patients who are unlikely to relapse in the absence of adjuvant therapy, in order to optimize treatment allocation and avoid unnecessary exposure to adverse effects. Furthermore, neoadjuvant immunotherapy strategies are rapidly expanding for melanomas at high risk of relapse. The goal in this challenge was to predict the occurrence of a relapse within the 5 years following initial diagnosis using digitized versions of microscopic slides and tabular clinical features. ## **2. Context of creation of the database:** The creation of the VisioMel database was supervised by the **[French Society of Pathology](https://www.sfpathol.org/)** along with the **[French Society of Dermatology](http://www.sfdermato.org/)**, the **[Cutaneous Cancer Group](http://www.sfdermato.org/groupe-5-groupe-de-cancerologie-cutanee-gcc)** (GCC) and the **[National Professional Council of Pathologists](http://www.cnpath.fr/)** (CNPath) in collaboration with the **[Health Data Hub](https://www.health-data-hub.fr/)** (HDH) as part of the [Data Challenge program](https://www.health-data-hub.fr/data-challenges). The VisioMel challenge occurs from May to June 2023. During this challenge, participants had to predict a score between 0 and 1 indicating the likelihood of relapse for each slide. ## **3. Target:** Data challenges are global competitions aimed at solving specific problems within a given time frame using highly anonymized data. Thus, these challenges are intended for data scientists (researcher, industrials, students etc.) from all around the world. The goal in this challenge was to predict whether a relapse will occur in the 5 years following the initial diagnosis using digitized versions of microscopic slides and tabular clinical features. For each slide, participants had to predict a score between 0 and 1 indicating the likelihood of relapse. ## **4. Results obtained from the database:** Challenge participants generated over 600 submissions, and the winning solutions achieved log loss scores of 0.39-0.40 (lower is better) compared to 0.50 for a benchmark model that predicted relapse from tabular clinical features. In terms of [area under the ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve), winning submissions substantially outperformed the benchmark with AUC scores surpassing 0.80. ![](https://drivendata.co/images/visiomel-roc.png)Figure 1. Area under the ROC curve of winning solutions. \ The results suggest that computer vision can detect signals in whole slide images (WSIs) that help make more accurate predictions of relapse months to years into the future. The [confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix) shows the number of different types of successes and errors. The raw counts highlight the class imbalance in the data: there are far more "no relapse" cases than relapses. \ To understand detection rates, we normalize the confusion matrix so that each cell shows the proportion of actual relapse/no relapse cases that were classified correctly/incorrectly. The bottom right cell (Actual: Yes / Predicted: Yes) shows the *true positive rate* or the proportion of relapses that were correctly detected. The top left cell (Actual: No / Predicted: No) shows the *true negative rate* or the proportion of "no relapse" cases that were correctly classified as no relapse. On average, solutions correctly detected around 73% of relapses in the test set and correctly identified around 75% of "no relapse" cases. ![](https://drivendata.co/images/visiomel-confusion-matrix-normalized.png)Figure 2. Confusion matrix associated with winning solutions. All the prize-winning solutions from this competition are linked below and made available for anyone to use and learn from. ## **5. Other informations:** #### **Data collection methodology and inclusion criteria:** Patients selection was made from the RIC-Mel database. Thanks to the efforts of a network of physicians from 49 French inclusion centers, this national database created in 2012 now collects data from around 40,000 patients with melanoma.\ \ Patients were selected according to the following criteria: * Cancer stage between 0 and IIC, * Initial diagnosis between 2012 and 2016 The data collection occurred in two main stages: 1. Updating the clinical variables and patient care pathway, including the search for potential relapse by patient-inclusive centers (hospital centers or cancer care centers within the RIC-Mel network). This stage underwent validation through consistency checks. 2. Unarchiving, de-identification, and digitization of histological slides corresponding to the excision of primary melanomas from these patients by pathology laboratories that diagnosed the melanoma. Clinical data along with digitized histological slides from nearly 2,000 patients were collected, anonymized, and made available to competitors for 7 weeks on a dedicated competition platform. #### Choice of variables: Challenge data include whole slide images (digitized versions of microscopic slides) and clinical variables. Whole slide images (WSIs) are digital formats that allow glass slides to be viewed, managed, shared, and analyzed. WSIs of melanomas are provided in the form of [pyramidal TIFs](https://www.loc.gov/preservation/digital/formats/fdd/fdd000237.shtml), which are a multi-resolution, tiled format. These extremely high resolution images can be quite large. You can find more information and tips for working with the data on the [data resources](https://www.drivendata.org/competitions/148/visiomel-melanoma/page/775/) page. Each patient is represented by one pyramidal TIF, which corresponds to one row in the metadata csv. The filename column is the unique identifier that connects the images to the metadata. Alongside the WSIs, you are provided with a metadata file that contains clinical variables collected from the patient at the time of initial diagnosis. These include demographic factors (e.g., age and sex) as well as some variables related to the tumor (e.g., its location and thickness). #### Detailed description of each field (name, type, meaning, possible values or range, unit of measurement): The clinical variables included in the VisioMel data challenge and their prognostic relevance are as follows : * Breslow (tumor thickness in millimeters at diagnosis): one of the most important factors for predicting the risk of relapse. Thicker melanomas have a worse prognosis than thinner ones.\ Type: string.\ Categories: <1 ; \[1 : 2\[ ; \[2 : 4\[ ; >=4\ Unit: millimeters. * Ulceration of the epidermis (total loss of epidermal tissue): the presence of ulceration is often associated with a poor prognosis. Indeed, ulcerated tumors tend to be more aggressive and present an increased risk of local or metastatic recurrence.\ Type: string.\ Categories: YES ; NO. * Age (age\_at\_initial\_diagnosis): patient’s age may play a role in predicting relapse. Younger patients may sometimes have a slightly higher risk of relapse.\ Type: string.\ Categories: <20 years ; \[20:30\[ ; \[30:40\[ ; \[40:50\[ ; \[50:60\[ ; \[60:70\[ ; \[70:80\[ ; >80\ Unit: year. * Sex: patient’s sex can influence the risk of relapse, although the impact may vary.\ Type: string.\ Categories: 1 ; 2 with 1 corresponding to male and 2 to female. * Tumor Location (melanoma\_body\_site): some anatomical sites may be more prone to relapse than others.\ Type: string.\ Categories: head/neck ; lower/upper limb ; trunk/pelvis. * Histological type of tumor: histological type influences the growth, clinical behavior, aggressiveness, and prognosis of melanomas.\ Type: string.\ Categories: ALM ; LMN/MLM ; NM ; SSM ; other/unclassified. * Family history of melanoma (melanoma\_history): individuals with a family history of melanoma have an increased risk of developing the disease themselves.\ Type: string.\ Categories: YES ; NO with YES corresponding to the presence of family history of melanoma and NO corresponding to the absence of family history of melanoma. * Genetic Factors (BRAF\_mutation): certain genetic mutations, such as those involved in the BRAF gene, have been linked to the risk of melanoma and relapse.\ Type: string.\ Categories: YES ; NO with YES corresponding to the presence of BRAF mutation and NO corresponding to the absence of BRAF mutation. * Patient status: whether the patient is alive or dead at the time of the cohorte conception (february 2023).\ Type: string.\ Categories: alive ; dead. The labels for this dataset come from patients' medical records, and indicate whether the patient was diagnosed with a melanoma relapse in the 5 years after initial diagnosis: * Locoregional relapse: recurrence or return of melanoma cancer cells in the nearby lymph nodes or tissues surrounding the original site of the primary tumor. This type of relapse indicates that the cancer has spread to the lymph nodes or tissues close to where it first developed, but has not yet metastasized to distant organs or parts of the body. \ Type: string.\ Categories: YES ; NO. * Delay between the initial diagnosis and the locoregional relapse (time\_btw\_initial\_diagnosis\_and\_locoregional\_relapse).\ Type: integral.\ Range: \[0-96]\ Unit: months. * Distant relapse: recurrence or reappearance of melanoma cancer cells in distant organs or parts of the body that are not near the site of the original tumor. This type of relapse indicates that the cancer has metastasized, spreading beyond the primary tumor site and nearby lymph nodes to other areas of the body, such as the lungs, liver, brain, or bones. Distant relapse is a significant progression of the disease and often carries a poorer prognosis compared to locoregional relapse or primary melanoma. * Delay between the initial diagnosis and the distant relapse (time\_btw\_initial\_diagnosis\_and\_distant\_relapse).\ Type: integral.\ Range: \[0-95]\ Unit: months. * Global relapse: corresponds to the first event of relapse that occurs after diagnosis (either locoregional or distant). * Delay between the initial diagnosis and the global relapse (time\_btw\_initial\_diagnosis\_and\_global\_relapse): either locoregional or distant.\ Type: integral.\ Range: \[0-105]\ Unit: months. #### Database structure and file rationale: The database has been divided into 5 files based on the value of Breslow. #### Limitations: The limitations of this dataset lie in the fact that many data points are missing, particularly regarding the presence or absence of the BRAF mutation. Additionally, the number of patients who have relapsed remains low, although it is close to real-world scenarios. #### Support: Project leader of VisioMel melanoma relapse challenge : <allalaurie@gmail.com> Scientific responsible : <fstaroz@ouestpathologie.fr> #### Credits: To help you understand how pathologists analyze a slide to predict relapse, we have provided 16 annotated slides on this page. The annotations show how clinical variables are measured and outline healthy tissue and lesions (but not necessarily all lesions present on a slide). See the [results announcement](https://drivendata.co/blog/visiomel-melanoma-winners) for more information on the winning approaches. An algorithm to detect artifact in the images is available [here](https://gitlab.com/healthdatahub/applications-du-hdh/pathology-slides-blur-detection). All of the prize-winning solutions from this competition are available for anyone to continue to use and learn from in the [Driven Data GitHub](https://github.com/drivendataorg/visiomel-melanoma). More information about melanoma analysis by pathologists, annotated slides and artifacts in images, tips and tricks, additional reading and research [here](https://www.drivendata.org/competitions/148/visiomel-melanoma/page/717/). #### Additional reading and research: * [An attention-based multi-resolution model for prostate whole slide image classification and localization](https://arxiv.org/pdf/1905.13208.pdf) - [Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies](https://www.nature.com/articles/s41379-018-0073-z) - [Assessment of machine learning of breast pathology structures for automated differentiation of breast cancer and high-risk proliferative lesions](https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2747694) - [Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens](https://www.nature.com/articles/s41598-020-66849-2) - [Assessment of machine learning detection of environmental enteropathy and celiac disease in children](https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2735765) ## **6. Licences:** Creative Commons Attribution ([CC BY 3.0](https://creativecommons.org/licenses/by/3.0/fr/deed.fr)) Licence Ouverte/Open Licence 2.0 ([Etalab 2.0](https://www.etalab.gouv.fr/wp-content/uploads/2017/04/ETALAB-Licence-Ouverte-v2.0.pdf)) ## **7. User form:** [USER FORM](https://docs.google.com/forms/d/e/1FAIpQLSdxXh01PyGL1n4i9qj90oTxcuiEu7RvWOHzbif6DtSlZI2_ow/viewform?usp=sf_link) The purpose of the user form is to track who (in terms of individuals and institutions) is using the data and potentially for what purposes. This form is not restrictive in the sense that access requests will never be denied. ## **7. Cite:** For any reuse of this database, use the DOI provided below: <https://doi.org/10.60597/wsg0-r316>
VisioMel database: predicting cutaneous melanoma relapse
File with clinical variables associated with (all Breslow)
Sample of metadata associated with diagnosed_conizations. Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
Metadata of undiagnosed_biopsies = 217 slides There are no labels for these images or corresponding annotations
Sameple of metadata associated with undiagnosed conizations. There are no labels for these images or corresponding annotations
Sample of images of diagnosed_conizations. Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
Metadata associated to diagnosed_biopsies (443 slides). Anatomical pathologists have labeled each slide according to four classes of lesion severity as classified by the World Health Organization: 0: benign (normal or subnormal) 1: low malignant potential (low grade squamous intraepithelial lesion) 2: high malignant potential (high grade squamous intraepithelial lesion) 3: invasive cancer (invasive squamous carcinoma) → It refers to the class of the most severe lesion on the slide (at the slide level, not annotation level).
Labels associted to annotated_biopsies (295 slides). Pathologists have labeled and annotated these images to point out regions that represent lesions. When working with the annotations, it's important to keep in mind the following points: - The annotated regions do not necessarily include all lesioned tissue in the slide. An unannotated region is not necessarily normal tissue. - The whole image class label and the annotation class label do not necessarily match. The annotated regions may be the image's labeled class or below. For instance, an image labeled as a class 2 lesion could have annotations representing class 0, 1, or 2. At least some of the annotated regions will represent the most severe/labeled class. All annotations on a slide with label 0 will be normal tissue. - The lesion may fall entirely within the square, or may extend beyond the annotation boundaries. - All annotations are a fixed size of 300x300 micrometers. As images have different resolutions in pixels/micrometer, annotations will have different dimensions in terms of pixels. - When using the geometries, it is important to know the origin of the coordinate system. Image processing software may assume the image origin is either the bottom left or the top left. The WKT shapes that we provide as annotations (geometry column in train_annotations.csv) are relative to the bottom left being the origin (0, 0).
Metadata of undiagnosed conizations (296 slides). There are no labels for these images or corresponding annotations
Metadata associted to annotated_biopsies (295 slides). Pathologists have labeled and annotated these images to point out regions that represent lesions. When working with the annotations, it's important to keep in mind the following points: - The annotated regions do not necessarily include all lesioned tissue in the slide. An unannotated region is not necessarily normal tissue. - The whole image class label and the annotation class label do not necessarily match. The annotated regions may be the image's labeled class or below. For instance, an image labeled as a class 2 lesion could have annotations representing class 0, 1, or 2. At least some of the annotated regions will represent the most severe/labeled class. All annotations on a slide with label 0 will be normal tissue. - The lesion may fall entirely within the square, or may extend beyond the annotation boundaries. - All annotations are a fixed size of 300x300 micrometers. As images have different resolutions in pixels/micrometer, annotations will have different dimensions in terms of pixels. - When using the geometries, it is important to know the origin of the coordinate system. Image processing software may assume the image origin is either the bottom left or the top left. The WKT shapes that we provide as annotations (geometry column in train_annotations.csv) are relative to the bottom left being the origin (0, 0).