{"doi":"10.1038/s41598-024-62776-8","title":"Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting","abstract":"Abstract Mayo Imaging Classification (MIC) for predicting future kidney growth in autosomal dominant polycystic kidney disease (ADPKD) patients is calculated from a single MRI/CT scan assuming exponential kidney volume growth and height-adjusted total kidney volume at birth to be 150 mL/m. However, when multiple scans are available, how this information should be combined to improve prediction accuracy is unclear. Herein, we studied ADPKD subjects ( $$n = 36$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>36</mml:mn> </mml:mrow> </mml:math> ) with 8+ years imaging follow-up (mean = 11 years) to establish ground truth kidney growth trajectory. MIC annual kidney growth rate predictions were compared to ground truth as well as 1- and 2-parameter least squares fitting. The annualized mean absolute error in MIC for predicting total kidney volume growth rate was $$2.1\\% \\pm 2\\%$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mn>2.1</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>2</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> compared to $$1.1\\% \\pm 1\\%$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mn>1.1</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ( $$p = 0.002$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.002</mml:mn> </mml:mrow> </mml:math> ) for a 2-parameter fit to the same exponential growth curve used for MIC when 4 measurements were available or $$1.4\\% \\pm 1\\%$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mn>1.4</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ( $$p = 0.01$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.01</mml:mn> </mml:mrow> </mml:math> ) with 3 measurements averaging together with MIC. On univariate analysis, male sex ( $$p = 0.05$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.05</mml:mn> </mml:mrow> </mml:math> ) and PKD2 mutation ( $$p = 0.04$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.04</mml:mn> </mml:mrow> </mml:math> ) were associated with poorer MIC performance. In ADPKD patients with 3 or more CT/MRI scans, 2-parameter least squares fitting predicted kidney volume growth rate better than MIC, especially in males and with PKD2 mutations where MIC was less accurate.","journal":"Scientific Reports","year":2024,"id":457820,"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":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9436,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":886068,"name":"Arman Sharbatdaran","orcid":"0000-0002-7097-0032","position":1,"is_corresponding":false},{"id":1069512,"name":"Xinzi He","orcid":"0000-0003-4492-7695","position":2,"is_corresponding":false},{"id":1267836,"name":"Chenglin Zhu","orcid":"0009-0003-6662-4908","position":3,"is_corresponding":false},{"id":381082,"name":"Jon D. Blumenfeld","orcid":"0000-0001-6230-4067","position":4,"is_corresponding":false},{"id":381084,"name":"Hanna Rennert","orcid":"0000-0002-1759-2562","position":5,"is_corresponding":false},{"id":1283333,"name":"Zhengmao Zhang","orcid":"0000-0003-4035-0331","position":6,"is_corresponding":false},{"id":1283755,"name":"Andrew Ramnauth","orcid":null,"position":7,"is_corresponding":false},{"id":698013,"name":"Daniil Shimonov","orcid":"0000-0002-0252-6474","position":8,"is_corresponding":false},{"id":886074,"name":"James M. Chevalier","orcid":"0000-0002-1376-4099","position":9,"is_corresponding":false},{"id":332011,"name":"Martin R. Prince","orcid":"0000-0002-9883-0584","position":10,"is_corresponding":false},{"id":808813,"name":"Zhongxiu Hu","orcid":"0009-0009-9258-5459","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T02:03:41.744499Z","pmid":"38877066","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":[]}