Evidence map›Paper›PMID 34203103›Full record

ArticleCancers2021

Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases.

Guido Costa, Lara Cavinato, Chiara Masci, Francesco Fiz, Martina Sollini, Letterio Salvatore Politi, Arturo Chiti, Luca Balzarini, Alessio Aghemo, Luca di Tommaso and 3 more

Registry-linked trialAbstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06779734 (Radiomic Features of Tumor and of Liver-Tumor Interface in Patients With Colorectal Liver Metastases. Identification of New Prognostic Biomarkers.), which is not on this map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06779734 completednot on this map

Radiomic Features of Tumor and of Liver-Tumor Interface in Patients With Colorectal Liver Metastases. Identification of New Prognostic Biomarkers.

TypeobservationalSponsorIstituto Clinico HumanitasRan2020 to 2024Enrolled400ConditionsLiver Metastases From Colorectal Cancer (mCRC)
3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023
    Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Review
  9. Review
  10. Article
  11. Review
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Guido CostaDivision of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20189 Milan, Italy.
Lara CavinatoMOX Laboratory, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0001-7007-0205
Chiara MasciMOX Laboratory, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy.
Francesco FizDepartment of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20189 Milan, Italy.
Martina SolliniDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy.
Letterio Salvatore PolitiDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy.
Arturo ChitiDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy.
Luca BalzariniDepartment of Radiology, IRCCS Humanitas Research Hospital, Rozzano, 20189 Milan, Italy.
Alessio AghemoDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy.
Luca di TommasoDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy.ORCID 0000-0002-9013-4728
Francesca IevaMOX Laboratory, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0003-0165-1983
Guido TorzilliDivision of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20189 Milan, Italy.ORCID 0000-0001-5798-5021
Luca ViganòDivision of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20189 Milan, Italy.ORCID 0000-0002-4108-4832

Funding

Associazione Italiana per la Ricerca sul Cancro 2019-23822
6 · The paper itself

Abstract

Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography. Seventy-eight patients were analyzed: 25 had grade 2-3 SinDil, 27 NRH, and 14 NASH. Three radiomic fingerprints independently predicted SinDil: GLRLM_f3 (OR = 12.25), NGLDM_f1 (OR = 7.77), and GLZLM_f2 (OR = 0.53). Combining clinical, laboratory, and radiomic data, the predictive model had accuracy = 82%, sensitivity = 64%, and specificity = 91% (AUC = 0.87 vs. AUC = 0.77 of the model without radiomics). Three radiomic parameters predicted NRH: conventional_HUQ2 (OR = 0.76), GLZLM_f2 (OR = 0.05), and GLZLM_f3 (OR = 7.97). The combined clinical/laboratory/radiomic model had accuracy = 85%, sensitivity = 81%, and specificity = 86% (AUC = 0.91 vs. AUC = 0.85 without radiomics). NASH was predicted by conventional_HUQ2 (OR = 0.79) with accuracy = 91%, sensitivity = 86%, and specificity = 92% (AUC = 0.93 vs. AUC = 0.83 without radiomics). In the validation set, accuracy was 72%, 71%, and 91% for SinDil, NRH, and NASH. Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves the diagnosis of CALI.

Indexed as

chemotherapy-associated liver injuriescolorectal liver metastasesdiagnostic imagingliver surgerynodular regenerative hyperplasiaradiomicssinusoidal dilatationsteatohepatitistextural featuresvirtual liver biopsy

Identifiers

PMID34203103
PMCPMC8234168

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.