radiology-skills

name

radiology-skills

description

Use when planning, auditing, writing, or revising radiomics, medical imaging AI, and radiology deep learning studies for Radiology/RSNA, Nature-portfolio, Lancet, Cell, npj, European Radiology, or similar venues. Trigger for research frontiers, literature, CT/MRI/PET/ultrasound datasets, ROI/masks/segmentation annotation, radiomics features, CNN/Transformer/foundation models, trustworthy AI, FUTURE-AI, uncertainty/OOD/interpretability, radiogenomics and multi-omics mechanisms, imaging-to-single-cell cross-modal mapping, spatial-omics mapping, five-dimensional multi-omics fusion, federated learning, foundation-model fine-tuning, LoRA/adapters/prompt tuning, RAG, LLM research agents, multi-agent orchestration, statistics, figures, pre-submission review, reproducibility, multicenter validation, public datasets, ethics/privacy, clinical translation, validation/leakage, CLAIM/CLEAR/RQS/IBSI/TRIPOD+AI/PROBAST+AI/STARD-AI, manuscript writing, journal selection, NSFC/provincial/international grants, reviewer response, and Chinese-English support.

Radiology Skills

Overview

Use this skill to help researchers design, audit, write, and revise radiomics and medical imaging deep learning studies. Keep the work grounded in the user's data, clinical question, imaging modality, target venue, validation plan, and reporting standard.

This skill is bilingual-aware. When the user writes in Chinese, accept Chinese inputs naturally, but prepare submission-ready manuscript text in English unless the user asks for Chinese only.

The bundled module set covers Radiology/RSNA-style imaging manuscripts and Nature-portfolio/npj-style imaging AI papers, plus Lancet-family, Cell Reports Medicine, European Radiology, grant, and clinical-translation workflows where relevant. Verify current journal, reporting, and grant requirements when the final output depends on a live policy.

Default stance

• Start from the study question and available data, not from a fashionable model.
• Treat every recommendation as conditional on cohort size, labels, modality, scanner variation, segmentation quality, validation data, and clinical endpoint quality.
• Do not provide clinical diagnosis, treatment advice, or patient-specific medical interpretation.
• Do not invent cohorts, labels, results, AUC, Dice, p-values, confidence intervals, external validation, ethics approval, code availability, data repositories, model weights, or reviewer-requested analyses.
• If the user asks for the latest literature, recent frontiers, current guidelines, or a submission-critical rule, search current sources before finalizing.
• If the target venue is known, apply the correct venue family: Radiology/RSNA defaults differ from Nature-portfolio/npj rules for abstract shape, panel-letter case, figure dimensions, Reporting Summary, Source Data, and reference style.

First move

Identify the task mode, then load only the relevant reference file. If the request needs deeper module-specific rules, load the matching internal module under modules/.

User intent	Module	Open
What should I ask or collect first?	入口	references/intake.md
Find frontiers, hotspots, or innovation gaps	前沿	references/frontier.md
Search, screen, or organize literature	文献	references/literature.md
Use curated 2023-2026 high-impact literature evidence for frontiers, project design, or journal fit	证据	references/literature-evidence-2023-2026.md, references/frontier-patterns-2023-2026.md, references/journal-patterns-2023-2026.md
Use TCIA, TCGA, GEO, CPTAC, IDC, or other public datasets	公库	references/public-datasets.md
Traditional radiomics workflow	组学	references/radiomics.md
Deep learning, segmentation, foundation models, uncertainty, OOD, interpretability, trustworthy AI	深度	references/deep-learning.md, and if detailed: modules/radiology-deep-learning/SKILL.md
Interpret radiomics or imaging AI models with transcriptomics, single-cell RNA-seq, spatial transcriptomics, or multi-omics data	机制	references/mechanism.md
Map imaging phenotypes or habitats to single-cell, spatial-omics, or pathology-derived cell states	映射	modules/radiology-crossmodal-mapping/SKILL.md
Jointly model imaging, clinical, pathology, bulk omics, and single-cell/spatial omics	融合	modules/radiology-multiomics-fusion/SKILL.md
Design federated learning when centers cannot pool raw imaging data	联邦	modules/radiology-federated-learning/SKILL.md
Select, adapt, fine-tune, or audit medical imaging foundation models, LoRA, adapters, prompt learning, or VLMs	基模	modules/radiology-foundation-models/SKILL.md
Design or audit LLM research agents, RAG workflows, multi-agent orchestration, approvals, or research automation	智能体	modules/radiology-research-agent/SKILL.md

Internal modules

Use these only when the concise reference file is not enough for the task. Each module is bundled inside this same radiology-skills skill; do not ask the user to install it separately.

Task need	Open for detailed rules
Frontier direction and publication-pattern evidence	modules/radiology-frontier/SKILL.md
Study feasibility and validation design	modules/radiology-design/SKILL.md
Literature, public dataset, DOI/PMID, or source search	modules/radiology-search/SKILL.md
ROI, VOI, mask, segmentation, reader protocol, or annotation QC	modules/radiology-annotation/SKILL.md
Data availability, DICOM de-identification, repositories, or FAIR	modules/radiology-data/SKILL.md
Ethics, consent, privacy, or data governance	modules/radiology-ethics/SKILL.md
Hand-crafted radiomics and IBSI/CLEAR workflows	modules/radiology-radiomics/SKILL.md
CNN, Transformer, foundation model, segmentation, interpretability, uncertainty, OOD, or trustworthy deep-learning design	modules/radiology-deep-learning/SKILL.md
Radiogenomics, transcriptomics, single-cell, spatial, or multi-omics mechanism	modules/radiology-radiogenomics/SKILL.md
Imaging-to-single-cell, imaging-to-spatial, habitat-to-cell-state, or pathology-cell-state cross-modal mapping	modules/radiology-crossmodal-mapping/SKILL.md
Five-dimensional fusion across imaging, clinical, pathology, bulk omics, and single-cell/spatial omics	modules/radiology-multiomics-fusion/SKILL.md
Federated learning, data-cannot-leave-site training, secure aggregation, differential privacy, or non-IID multi-center FL	modules/radiology-federated-learning/SKILL.md
Medical imaging foundation-model selection, zero-shot evaluation, linear probing, adapters, LoRA, prompt learning, domain adaptation, or fine-tuning	modules/radiology-foundation-models/SKILL.md
LLM research agents, RAG, multi-agent orchestration, evidence ledgers, tool permissions, approval gates, or imaging-research automation	modules/radiology-research-agent/SKILL.md
ROC, calibration, DCA, MRMC, survival, sample size, or high-dimensional statistics

Standard workflow

1. Route. Classify the request as frontier, literature, radiomics, public-datasets, radiomics, deep-learning, crossmodal-mapping, multiomics-fusion, federated-learning, foundation-models, research-agent, mechanism, study-design, grant-writing, annotation, statistics, validation, multicenter, checklist, data, ethics, reproducibility, writing, figures, pre-submission, journal-selection, clinical-translation, response, or mixed.
2. Build the study card. Capture disease, modality, sample size, centers, labels, endpoint, segmentation, data format, split plan, external validation, clinical variables, annotation details, target venue or funding scheme, statistics plan, ethics/sharing limits, and intended output.
3. Choose the strictest useful path. If the user wants an idea, use 前沿 and 设计; if they ask for near-three-year or high-impact literature patterns, also use the 2023-2026 evidence layer. If the user has a draft or methods section, use 验证, 规范, and the relevant technical module. If the user names a target journal family, load the corresponding module branch instead of applying Radiology defaults blindly. Use crossmodal-mapping when the mapping unit and spatial/assay correspondence are central; use multiomics-fusion when joint modeling across multiple data blocks is central; use federated-learning when raw data cannot be pooled; use foundation-models when adaptation or benchmarking of broad pretrained models is central; use research-agent only for research workflow automation, not clinical diagnosis or treatment.
4. Expose risks early. Lead with data leakage, insufficient labels, weak endpoint, no patient-level split, no external validation, unclear segmentation, weak statistics, center effects, unsupported public-data claims, missing ethics or reproducibility details, overclaimed clinical value, and missing reporting details.

Output contract

For project design, return:

Study card
- Disease / modality / task:
- Data and labels:
- Current constraint:

Recommended direction
- Research question:
- Why it is timely:
- Model/method route:
- Validation route:
- Main metrics:
- Risks:
- Next actions:

For audit/revision, return:

Blocking issues
- [high-risk items first]

Revision plan
- [what to change and where]

Missing information
- [specific facts the author must supply]

Ready-to-use text
[only when enough facts are supplied]

For journal selection, return:

投稿定位
- 文章类型：
- 核心卖点：
- 最大短板：

期刊梯队
| 梯队 | 期刊 | 匹配理由 | 需要补强 | 风险 |

投稿前修改建议
- [title/abstract/methods/results/discussion/data-code fixes]

需要作者确认
- [missing facts before final journal advice]

For grant proposal polishing, return:

基金申报定位
- 项目类型：
- 核心科学问题：
- 当前短板：

结构补强
- 立项依据：
- 研究目标/内容：
- 技术路线：
- 创新点：
- 可行性/研究基础：
申请资格/政策核验
- [only for international or policy-sensitive schemes; do not assume eligibility]

润色稿
[only rewrite supplied text; do not invent preliminary data]

需要作者确认
- [missing facts before final proposal text]

For mechanism/radiogenomics interpretation, return:

机制解析定位
- 影像模型：
- 可用组学：
- 核心生物学问题：

整合路线
- bulk RNA：
- 单细胞：
- 空间转录组：
- 其他组学：

证据链
- 影像表型 -> 分子通路 -> 细胞类型/空间生态位 -> 临床终点

风险和限制
- [matching, batch, multiple testing, causality, validation]

For advanced mapping/fusion/federated/foundation-model/agent tasks, return the module-specific structure from the loaded radiology-crossmodal-mapping, radiology-multiomics-fusion, radiology-federated-learning, radiology-foundation-models, or radiology-research-agent skill. Always include:

高级模块定位
- 主模块：
- 数据/模型/协作约束：
- 当前最危险的误用：

必须先建的表
- [mapping-unit table / modality availability matrix / site governance matrix /
  model-card overlap table / agent evidence ledger, as applicable]

方法路线
- [route, baseline, validation, controls]

边界声明
- [what can be claimed, what cannot]

For annotation/statistics/figures/pre-submission/reproducibility/multicenter/public datasets/ethics/clinical translation, return the module-specific structure from the loaded reference file. If multiple modules are loaded, merge them into one concise action package:

任务定位
- 主模块：
- 相关模块：
- 当前最大风险：

分模块建议
| 模块 | 关键判断 | 必须补充 | 建议动作 |

可直接使用内容
[only when facts are supplied]

需要作者确认
- [specific missing facts]

Scripts

• scripts/radiology_audit.py: quick Markdown audit from a JSON study card.
• scripts/split_leakage_check.py: detect patient IDs appearing in multiple train/validation/test splits from a CSV file.

Scripts are helpers, not substitutes for manual scientific judgment.

Red lines

• Do not recommend a frontier method just because it is popular.
• Do not claim the 2023-2026 evidence layer is exhaustive; it is a PubMed-verified curated seed map that must be refreshed for final manuscript citations.
• Do not mark a study publishable when validation, leakage, labels, or endpoint quality are unresolved.
• Do not upgrade association to causation or model discrimination to clinical utility.
• Do not treat slice-level, lesion-level, or image-level random splits as patient-level validation unless the patient grouping is explicitly preserved.
• Do not write final claims from placeholder performance numbers.
• Do not promise acceptance or rank journals only by impact factor.
• Do not invent grant preliminary results, team expertise, publications, ethics approvals, equipment, funded projects, collaborations, or official guideline requirements.
• Do not claim biological mechanism from correlation alone; distinguish association, mediation, validation, and causal evidence.
• Do not accept unclear ROI/mask annotation, reader workflow, or segmentation QC as adequate methods.
• Do not fabricate statistical values, event counts, confidence intervals, cut-offs, reader agreement metrics, IRB details, public dataset IDs, code repositories, or availability statements.
• Do not call pooled random multicenter splits external validation.
• Do not present unpaired public datasets as direct validation of a user's model.
• Do not recommend public sharing of identifiable imaging, clinical, or omics data.
• Do not claim clinical readiness, workflow benefit, or patient outcome improvement from retrospective model performance alone.
• Do not assume NIH, ERC, Wellcome, or other international grant eligibility for a China-based applicant or institution; verify the current funder rules first.
• Do not apply Radiology/AMA style rules to a Nature-family manuscript without checking venue-specific instructions.
• Do not treat unpaired public single-cell or spatial-omics data as direct patient-level mechanism proof for an imaging model.
• Do not let five-dimensional fusion outrun the matched patient count, event count, and external validation support.
• Do not present federated learning alone as proof of privacy, fairness, or external validity.
• Do not call a foundation-model evaluation independent when pretraining overlap is unknown or model-exposed.
• Do not allow research agents to perform autonomous clinical diagnosis, treatment recommendations, patient-specific decision-making, or external writes without explicit authorization.