A 12-step interpretive-reading scaffold for challenging and rare-disease radiology: educational perspective based on 47 image-pathology components

{"Radiologists":[0],"are":[1,61,79],"vulnerable":[2],"to":[3,16,37,67],"\\"common-disease-first\\"":[4],"cognitive":[5],"bias":[6],"when":[7],"interpreting":[8],"challenging":[9],"and":[10,18,76,81],"rare-disease":[11,43],"cases,":[12],"which":[13],"may":[14],"lead":[15],"misdiagnosis":[17],"missed":[19],"diagnosis1,2,3.":[20],"This":[21,45],"article":[22],"proposes":[23],"a":[24,32,47],"streamlined":[25],"structured":[26],"12-step":[27],"interpretive-reading":[28],"scaffold":[29],"built":[30],"upon":[31],"47-component":[33],"image-pathology":[34],"decomposition":[35],"toolkit":[36],"structure":[38],"the":[39,69],"reasoning":[40],"chain":[41],"for":[42,63],"imaging.":[44],"is":[46,88],"purely":[48],"theoretical":[49,65],"educational":[50],"manuscript":[51],"without":[52],"any":[53],"real-patient":[54],"datasets.":[55],"Six":[56],"representative":[57],"cross-system":[58],"rare":[59],"diseases":[60],"adopted":[62],"simulated":[64],"demonstrations":[66],"illustrate":[68],"practical":[70],"logic":[71],"of":[72],"this":[73],"workflow.":[74],"Advantages":[75],"explicit":[77],"limitations":[78],"discussed,":[80],"further":[82],"validation":[83],"by":[84],"multicentre":[85],"clinical":[86],"cohorts":[87],"required.":[89]}

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-01
DOI
https://doi.org/10.5281/zenodo.22235779
Primary Topic
Genomics and Rare Diseases
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
preprint

A 12-step interpretive-reading scaffold for challenging and rare-disease radiology: educational perspective based on 47 image-pathology components

Zhongfeng
Zenodo (CERN European Organization for Nuclear Research)
Genomics and Rare Diseases
preprint

A 12-step interpretive-reading scaffold for challenging and rare-disease radiology: educational perspective based on 47 image-pathology components

Zhongfeng
preprint en

Abstract

Radiologists are vulnerable to "common-disease-first" cognitive bias when interpreting challenging and rare-disease cases, which may lead to misdiagnosis and missed diagnosis1,2,3. This article proposes a streamlined structured 12-step interpretive-reading scaffold built upon a 47-component image-pathology decomposition toolkit to structure the reasoning chain for rare-disease imaging. This is a purely theoretical educational manuscript without any real-patient datasets. Six representative cross-system rare diseases are adopted for simulated theoretical demonstrations to illustrate the practical logic of this workflow. Advantages and explicit limitations are discussed, and further validation by multicentre clinical cohorts is required.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Genomics and Rare Diseases
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.