Review Comment:
Summary:
The work begins motivating digital mechanisms involved in processing digital pathology images, highlighting problems related availability of information regarding pathological observations as free text and annotation.
To deal with these limitations, convolutional neural networks (CNN) and natural language processing (NLP) are presented as common solutions to the problems pointed out. However, these approaches require extensive data available - what brings to surface other problems due to data variability - and extensive testing.
Ontologies are motivated as a feasible solution for digital pathology representation aiming at the tasks, considering that they are used together with other techniques such as NLP and Deep Learning. Thus, the use of ontologies for histopathology are motivated to support terminological standardization in the field.
A new ontology is proposed to represent four pathological processes of interest: colon cancer, uterine cervix cancer, lung cancer and celiac disease. Its scope incorporates patient data, clinical trial data she might participate, the outcome of the diagnosis, the anatomical location, tests performed on patients and further annotations about the disease.
Major problems:
• Why a related work and a background? Why not make them a single section? This makes the article quite long and the relevant parts are delayed for readers, having first to read almost 8 pages with introduction, motivation, related work, and background.
• Multiple motivations related to the ExaMode project available with similar contents.
• Reuse of entities are not described in detail in section 4. It is recommended to look on MIREOT (https://content.iospress.com/articles/applied-ontology/ao087) to properly refer to external ontologies.
• It is unclear how sections/modules/classes and object properties/relations were reused from other ontologies.
• On what were all classes and models representations based?
• “Disease or disorder” class, from a representational perspective, was previously described in an article from S. Schulz and collaborators (https://jbiomedsem.biomedcentral.com/articles/10.1186/2041-1480-2-S2-S6) and may be seen problematic here.
• Why using DOID for representing “Disease or Disorder” when (for instance) we have SNOMED CT as a generally accepted representation for this?
• No reference to which ontology engineering methodology (NeOn, Methontology, among others) and principles (OBO Principles) was employed.
• Ontology Language? Expressivity?
• Several ontologies were put together to support the ExaMode Ontology. However, it is unclear to which top-level these “imported” classes refer. They could be mapped to Basic Formal Ontology v.2 (BFO2), once most referred ontologies and classes come from the biomedical domain and refer to OBO ontologies. Classes taken from multiple ontologies should be harmonized under a common ontological reference (for instance, BFO2, BTL2 for the biomedical domain, UFO, GFO, among others). For the presented ontology, unfortunately this seems not to be the case.
• There are no clear criteria on how ontologies were included to be reused.
• Explanations regarding ExaMode cases, Diagnosis and other sections, performed by means of listing classes names and explaining them are unproductive. The authors present their hierarchy to illustrate, but reading all explanation about how to read the image is not ideal. It is more relevant to represent the complex issues together with motivating explanations and present them together with images and clear axioms.
• The representation of Annotation may seem odd, considering authors explanation to it. Entities from regular ontologies are used and declared directly in database entries (for instance, UniProt uses GO for Annotations) or in clinical texts (such as reports) to replace free text entities with proper ontological ones. With this explanation in mind, why modelling Annotation? NLP tasks are going to seek “cervix cancer”, “polyp” among other specific histopathology classes that must be already represented in ExaMode Ontology.
• Foundational Model of Anatomy (FMA) is regularly used for representing anatomical entities.
• In the *.owl file, why are pathological processes modelled as instances?
• Who reviewed the ontology?
• How could the authors guarantee that the whole representation is adequate to the usage envisioned and ontologically correct (w.r.t. an ontological top-level)?
Minor problems:
• Paragraph organization in introduction can be organized properly. For instance, the second and third paragraphs should be together. Both contains single phrases that are complimentary;
• SNOMED CT is already a brand. It does not require to be identified as Standardized Nomenclature of Medicine – Clinical Terms;
• What do authors want to mean with “… holistically models…”? Unclear. Be precise.
• “… production was expected to be over 2k exabytes in 2020;…” We are in 2022.
• “The lack of labelled data, which are expensive and time-consuming to produce…” Could you clarify if you are saying that no-labelled data is time consuming and expensive to consume?
• To reuse ontology sections (also called modules) directly into the ontology, authors may want to take a look in an ontology modularity approach available here;(https://sites.google.com/site/ontologymodularity/). It may seem old, but it still works with certain protégé versions (java 8+ I guess);
• Marsh-Oberhuber and Corazza-Villanacci classification systems were not referenced in p8, l19-20;
• Visualization of clinical report with ExaNet is not completely clear. It may look better if only class names are used.
Recommendations for the authors:
• Organize the methods section considering proper ontology development methodologies and approaches frequently used for biomedical ontologies, as previously cited;
• Organize presentation of the ontology main representation aspects, pointing out the complex axioms and how it contributes to the histopathology landscape;
• This article seems mainly to focus on presenting an ontology; neither NLP tasks, nor visualization. I suggest to split this article in 2: (i) the ontology (which is the main subject of the current version), and (ii) putting the ontology into use for histopathology.
Reasons for rejection:
• The article requires new, deeper evaluation of the approach regarding properly using known ontology development methodologies;
• Reuse of ontological entities unclear;
• Lack of clear validation mechanisms;
• Lack of clear explanation on representational choices.
• It may be of interest for publication for the biomedical audience; however, it currently lacks basic methodological basis.
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