18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - A prospective externally validated study.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:29494598", "kind": "science-record", "revision": 4, "content_hash": "sha256:450a6f8bff599d87183aaf3afc94525c6b3277b5201246c8e071b26153b84113", "created_at": "2026-11-01T15:40:32Z", "updated_at": "2026-11-01T21:21:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - A prospective externally validated study.", "doi": "10.1371/journal.pone.0192859", "pmid": "29494598", "authors": ["Sara Carvalho", "Ralph T H Leijenaar", "Esther G C Troost", "Janna E van Timmeren", "Cary Oberije", "Wouter van Elmpt", "Lioe-Fee de Geus-Oei", "Johan Bussink", "Philippe Lambin"], "journal": "PloS one", "year": 2018, "abstract": "BACKGROUND: Lymph node stage prior to treatment is strongly related to disease progression and poor prognosis in non-small cell lung cancer (NSCLC). However, few studies have investigated metabolic imaging features derived from pre-radiotherapy 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET) of metastatic hilar/mediastinal lymph nodes (LNs). We hypothesized that these would provide complementary prognostic information to FDG-PET descriptors to only the primary tumor (tumor).\nMETHODS: Two independent cohorts of 262 and 50 node-positive NSCLC patients were used for model development and validation. Image features (i.e. Radiomics) including shape and size, first order statistics, texture, and intensity-volume histograms (IVH) (http://www.radiomics.io/) were evaluated by univariable Cox regression on the development cohort. Prognostic modeling was conducted with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), automatically selecting amongst FDG-PET-Radiomics descriptors from (1) tumor, (2) LNs or (3) both structures. Performance was assessed with the concordance-index. Development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b).\nRESULTS: Common SUV descriptors (maximum, peak, and mean) were significantly related to overall survival when extracted from LNs, as were LN volume and tumor load (summed tumor and LNs' volumes), though this was not true for either SUV metrics or tumor's volume. Feature selection exclusively from imaging information based on FDG-PET-Radiomics, exhibited performances of (1) 0.53 -external 0.54, when derived from the tumor, (2) 0.62 -external 0.56 from LNs, and (3) 0.62 -external 0.59 from both structures, including at least one feature from each sub-category, except IVH.\nCONCLUSION: Combining imaging information based on FDG-PET-Radiomics features from tumors and LNs is desirable to achieve a higher prognostic discriminative power for NSCLC.", "claims": [{"id": "claim-method", "statement": "A prognostic model was fitted to the data with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), selecting amongst PET-Radiomics descriptors extracted from the tumor (model 1), LN (model 2) and the union of both structures (model 3) [18].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29494598", "locator": "sections[14] \"Multivariable analysis\" paragraph 2", "quote": "A prognostic model was fitted to the data with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), selecting amongst PET-Radiomics descriptors extracted from the tumor (model 1), LN (model 2) and the union of both structures (model 3) [18]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29494598", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used 10-fold cross-validated LASSO to select FDG-PET radiomics features; the combined tumor-and-lymph-node model achieved a development C-index of 0.62 and an external-validation C-index of 0.59, and the development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b).", "kind": "quantitative", "target": {"value": 10.0, "unit": "fold change", "tolerance": null, "source": "Prognostic modeling was conducted with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), automatically selecting amongst FDG-PET-Radiomics descriptors from (1) tumor, (2) LNs or (3) both structures."}, "evidence": [{"asset": "source:pmid:29494598", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Prognostic modeling was conducted with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), automatically selecting amongst FDG-PET-Radiomics descriptors from (1) tumor, (2) LNs or (3) both structures."}, {"asset": "source:pmid:29494598", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Feature selection exclusively from imaging information based on FDG-PET-Radiomics, exhibited performances of (1) 0.53 –external 0.54, when derived from the tumor, (2) 0.62 –external 0.56 from LNs, and (3) 0.62 –external 0.59 from both structures, including at least one feature from each sub-category, except IVH."}, {"asset": "source:pmid:29494598", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Combining imaging information based on FDG-PET-Radiomics features from tumors and LNs is desirable to achieve a higher prognostic discriminative power for NSCLC."}, {"asset": "source:pmid:29494598", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study used 10-fold cross-validated LASSO to select FDG-PET radiomics features; the combined tumor-and-lymph-node model achieved a development C-index of 0.62 and an external-validation C-index of 0.59, and the development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:29494598"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:29494598", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0192859", "sha256": "sha256:d818e4195ab7ccc816a3f8ed215b8b42123c33aebf2f73ace02926264b9c9de7", "bytes": 30350, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1371/journal.pone.0192859", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC5832210", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:29494598:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:169d2cfd528ae6a05668a5517a4f23bbd73f58d3963200135259a5f00b98b0cb", "bytes": 239, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[16]/table-wrap[1]#pone.0192859.t001", "version": null, "note": "Demographics and clinical information of development and validation cohorts.", "structure": {"pmcid": "PMC5832210", "section_index": 16, "section_title": "Results", "row_count": 0, "column_count": 0}, "table": {"label": "Table 1", "caption": "Demographics and clinical information of development and validation cohorts.", "rows": [], "row_count": 0, "column_count": 0, "numeric_cells": 0, "header_rows": 0, "locator": "section[16]/table-wrap[1]#pone.0192859.t001"}}, {"id": "asset:table:pmid:29494598:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:4f495791e8e0b36bf5000b0603271a61a4301a61ce49d6a03c8ae3df93e70ade", "bytes": 234, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[16]/table-wrap[2]#pone.0192859.t002", "version": null, "note": "Univariable Cox regression of clinical variables in development cohort.", "structure": {"pmcid": "PMC5832210", "section_index": 16, "section_title": "Results", "row_count": 0, "column_count": 0}, "table": {"label": "Table 2", "caption": "Univariable Cox regression of clinical variables in development cohort.", "rows": [], "row_count": 0, "column_count": 0, "numeric_cells": 0, "header_rows": 0, "locator": "section[16]/table-wrap[2]#pone.0192859.t002"}}, {"id": "asset:table:pmid:29494598:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:89ae1a2d8663c28b7777a934f0c0a0bf201310a379a364343b1f599bd9bf18e3", "bytes": 267, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[16]/table-wrap[3]#pone.0192859.t003", "version": null, "note": "Distribution of common PET descriptors (maximum, peak and mean) and volume of the primary tumor and LNs.", "structure": {"pmcid": "PMC5832210", "section_index": 16, "section_title": "Results", "row_count": 0, "column_count": 0}, "table": {"label": "Table 3", "caption": "Distribution of common PET descriptors (maximum, peak and mean) and volume of the primary tumor and LNs.", "rows": [], "row_count": 0, "column_count": 0, "numeric_cells": 0, "header_rows": 0, "locator": "section[16]/table-wrap[3]#pone.0192859.t003"}}, {"id": "asset:table:pmid:29494598:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:8c403a625ac2778425fab00baf17da76d29c110ee436fe5f30d69f49bfff461c", "bytes": 311, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[16]/table-wrap[4]#pone.0192859.t004", "version": null, "note": "Distribution of features included in the Cox regression model for FDG-PET-CT-based features extracted from pre-radiotherapy scans of NSCLC patients.", "structure": {"pmcid": "PMC5832210", "section_index": 16, "section_title": "Results", "row_count": 0, "column_count": 0}, "table": {"label": "Table 4", "caption": "Distribution of features included in the Cox regression model for FDG-PET-CT-based features extracted from pre-radiotherapy scans of NSCLC patients.", "rows": [], "row_count": 0, "column_count": 0, "numeric_cells": 0, "header_rows": 0, "locator": "section[16]/table-wrap[4]#pone.0192859.t004"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Cox regression"}}, {"id": "method-2", "family": null, "specific": null, "label": "10-fold cross-validated least absolute shrinkage and selection operator (LASSO)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "A prognostic model was fitted to the data with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), selecting amongst PET-Radiomics descriptors extracted from the tumor (model 1), LN (model 2) and the union of both structures (model 3) [18]."}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "cox_ph", "expected_value": 10.0, "unit": "fold change", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["Development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b)."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:29494598", "asset:table:pmid:29494598:1", "asset:table:pmid:29494598:2", "asset:table:pmid:29494598:3", "asset:table:pmid:29494598:4"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:33:46Z", "independence": "same_provider"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 8, "usage": {"prompt_tokens": 56561, "completion_tokens": 4687, "cached_tokens": 1816}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:29494598", "type": "paper", "label": "pmid:29494598"}, {"id": "source:pmid:29494598", "type": "manuscript", "label": "PMID 29494598 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:29494598", "type": "paper", "label": "pmid:29494598"}, {"id": "source:pmid:29494598", "type": "manuscript", "label": "PMID 29494598 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-29494598", "type": "extraction", "used": ["source:pmid:29494598"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:29494598", "parent_hash": "sha256:40c8597b104bfa6f08f59b583c6d9a0c991f2bb54b69dbe9d083c190b74453ed", "fork_of": null, "generation": 3}, "gaps": ["execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["Cox regression"], "order_basis": "analysis_only", "id": "assay:1"}]}