{"doi":"10.3390/ai7060228","title":"Phenotyping of Histology Imaging Data with Histomics","abstract":"<jats:p>Whole-slide imaging has transformed histopathology into a data-rich domain; however, many computational pathology models encode tissue morphology within latent representations, limiting interpretability, reproducibility, and generalization. This review positions histomics as an intermediate phenotype representation layer linking histological images with downstream clinical inference through structured descriptors of tissue morphology, spatial organization, and tissue architecture. Unlike prior reviews focused primarily on feature extraction or predictive performance, the study adopts a representation-centric perspective of histomics. A taxonomy of histomic features across biological scales is presented, and artificial intelligence frameworks, including machine learning, deep learning, weakly supervised learning, and multimodal approaches, are systematically examined. Key challenges, including segmentation dependence, feature instability, aggregation variability, and domain shift, are critically analyzed alongside emerging developments in foundation models, representation learning, and multimodal pathology. The review provides a unified framework for understanding histomic representations and identifies future directions for developing robust, interpretable, and generalizable computational pathology systems.</jats:p>","journal":"AI","year":2026,"id":639044,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1660103,"name":"Deepshikha Bhati","orcid":"0009-0002-0115-6026","position":1,"is_corresponding":false},{"id":1660104,"name":"Deepak Kumar Shukla","orcid":"0009-0008-3813-4959","position":2,"is_corresponding":false},{"id":1660102,"name":"Fnu Neha","orcid":"0009-0004-3702-2382","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Phenotyping of Histology Imaging Data with Histomics","abstract":"<jats:p>Whole-slide imaging has transformed histopathology into a data-rich domain; however, many computational pathology models encode tissue morphology within latent representations, limiting interpretability, reproducibility, and generalization. This review positions histomics as an intermediate phenotype representation layer linking histological images with downstream clinical inference through structured descriptors of tissue morphology, spatial organization, and tissue architecture. Unlike prior reviews focused primarily on feature extraction or predictive performance, the study adopts a representation-centric perspective of histomics. A taxonomy of histomic features across biological scales is presented, and artificial intelligence frameworks, including machine learning, deep learning, weakly supervised learning, and multimodal approaches, are systematically examined. Key challenges, including segmentation dependence, feature instability, aggregation variability, and domain shift, are critically analyzed alongside emerging developments in foundation models, representation learning, and multimodal pathology. The review provides a unified framework for understanding histomic representations and identifies future directions for developing robust, interpretable, and generalizable computational pathology systems.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W7165202909","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.77710358,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.3390/ai7060228","host_type":"journal"},{"url":"https://doi.org/10.3390/ai7060228","host_type":"publisher"},{"url":"https://www.mdpi.com/2673-2688/7/6/228/pdf","host_type":"publisher"},{"url":"https://doaj.org/article/65d91a90f37343a4839b470fc0d7f61d","host_type":"repository"}],"fields_of_study":["AI in cancer detection","Cell Image Analysis Techniques","Digital Imaging for Blood Diseases"],"mesh_terms":[],"keywords":["Segmentation","Inference","Feature (linguistics)","Feature extraction","Representation (politics)","Pattern recognition (psychology)","Medical imaging","Digital pathology","Domain (mathematical analysis)"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T22:20:04.799880Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}