Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use

Radiology Artificial Intelligence, Год журнала: 2024, Номер 6(4)

Опубликована: Май 8, 2024

Radiomics is a promising and fast-developing field within oncology that involves the mining of quantitative high-dimensional data from medical images. has potential to transform cancer management, whereby radiomics can be used aid early tumor characterization, prognosis, risk stratification, treatment planning, response assessment, surveillance. Nevertheless, certain challenges have delayed clinical adoption acceptability in routine practice. The objectives this report are (

Язык: Английский

---

The TRIPOD-LLM reporting guideline for studies using large language models

Nature Medicine, Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

Язык: Английский

---

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

Diagnostic and Interventional Radiology, Год журнала: 2024, Номер unknown

Опубликована: Июль 2, 2024

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into practice may present a double-edged sword due to bias (i.e., systematic errors).AI algorithms have the potential mitigate cognitive biases in human interpretation, but extensive research has highlighted tendency of AI systems internalize within model.This fact, whether intentional or not, ultimately lead unintentional consequences clinical setting, potentially compromising patient outcomes.This concern is particularly important imaging, where been more progressively and widely embraced than any other field.A comprehensive understanding at each stage pipeline therefore essential contribute developing solutions that are not only less biased also applicable.This international collaborative review effort aims increase awareness imaging community about importance proactively identifying addressing prevent its negative from being realized later.The authors began with fundamentals by explaining different definitions delineating various sources.Strategies detecting were then outlined, followed techniques avoidance mitigation.Moreover, ethical dimensions, challenges encountered, prospects discussed.

Язык: Английский

---

The widening gap between radiomics research and clinical translation: rethinking current practices and shared responsibilities

Опубликована: Янв. 1, 2025

Язык: Английский

---

Ovarian cancer staging and follow-up: updated guidelines from the European Society of Urogenital Radiology female pelvic imaging working group

European Radiology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Abstract Objective To provide up-to-date European Society of Urogenital Radiology (ESUR) guidelines for staging and follow-up patients with ovarian cancer (OC). Methods Twenty-one experts, members the female pelvis imaging ESUR subcommittee from 19 institutions, replied to 2 rounds questionnaires regarding techniques structured reporting used pre-treatment evaluation OC patients. The results survey were presented other authors during group’s annual meeting. lexicon was aligned American (SAR)-ESUR lexicon; a first draft circulated, then comments suggestions incorporated. Results Evaluation disease extent at diagnosis should be performed by chest, abdominal, pelvic CT. radiological report map specific mention sites that may preclude optimal cytoreductive surgery. For suspected recurrence, CT [ 18 F]FDG PET-CT are both valid options. MRI can considered in experienced centres, as an alternative CT, considering high costs need higher expertise reporting. Conclusions is modality choice preoperative A report, including debulking, value patient management. Key Points Question Guidelines last published (OC) 2010; here, guidance on reporting, incorporating advances field, provided. Findings Structured reports out disease, highlighting limit cytoreduction. 18FDG options, considered. Clinical relevance Imaging initial (mainly based CT), using considers surgical needs valuable treatment selection planning.

Язык: Английский

---

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative

European Radiology Experimental, Год журнала: 2024, Номер 8(1)

Опубликована: Май 14, 2024

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, can be accomplished with systematic use guidelines. The CheckList for EvaluAtion Radiomics (CLEAR) was previously developed assist authors their radiomic reviewers evaluation. To take full advantage CLEAR, further explanation elaboration each item, well literature examples, may useful. main goal this work, Explanation Elaboration Examples CLEAR (CLEAR-E3), improve CLEAR's usability dissemination. In international collaborative effort, members the European Society Medical Imaging Informatics-Radiomics Auditing Group searched identify representative examples item. At least two demonstrating optimal reporting, were presented All selected from open-access articles, allowing users easily consult corresponding full-text articles. addition these, item's expanded elaborated. For easier access, resulting document available at https://radiomic.github.io/CLEAR-E3/ . As complementary effort we anticipate that initiative will greater ease transparency, editors reviewing manuscripts.Relevance statement Along original checklist, CLEAR-E3 expected provide more in-depth understanding items, concrete evaluating research.Key points• aims research, manuscripts.• Based on positive by EuSoMII Group, item elaborated CLEAR-E3.• accessed

Язык: Английский

---

The TRIPOD-LLM Statement: A Targeted Guideline For Reporting Large Language Models Use

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июль 25, 2024

Large Language Models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present TRIPOD-LLM, an extension of the TRIPOD+AI statement, addressing unique challenges LLMs biomedical applications. TRIPOD-LLM provides a comprehensive checklist 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce modular format accommodating various LLM research designs tasks, with 14 32 subitems applicable across all categories. Developed through expedited Delphi process expert consensus, emphasizes transparency, human oversight, task-specific performance reporting. also interactive website ( https://tripod-llm.vercel.app/ ) facilitating easy guideline completion PDF generation for submission. As living document, will evolve field, aiming enhance quality, reproducibility, clinical applicability healthcare

Язык: Английский

---

Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes

European Radiology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 9, 2025

Abstract Objectives To investigate how studies determine the sample size when developing radiomics prediction models for binary outcomes, and whether meets estimates obtained by using established criteria. Methods We identified that were published from 01 January 2023 to 31 December in seven leading peer-reviewed radiological journals. reviewed justification methods, actual used. calculated compared used three criteria proposed Riley et al. investigated which characteristics factors associated with sufficient Results included 116 studies. Eleven out of one hundred sixteen justified size, 6/11 performed a priori calculation. The median (first third quartile, Q1, Q3) total is 223 (130, 463), those training are 150 (90, 288). (Q1, difference between minimum according −100 (−216, 183), differences more restrictive approach based on −268 (−427, −157). presence external testing specialty topic size. Conclusion Radiomics often designed without justification, whose may be too small avoid overfitting. Sample encouraged model. Key Points Question critical help minimize overfitting model, but overlooked underpowered research . Findings Few justified, calculated, or reported their most them did not meet recent formal Clinical relevance justification. Consequently, many It should justify, perform, report considerations

Язык: Английский

---

Evaluating the impact of the Radiomics Quality Score: a systematic review and meta-analysis

European Radiology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 10, 2025

Abstract Objectives Conduct a systematic review and meta-analysis on the application of Radiomics Quality Score (RQS). Materials methods A search was conducted from January 1, 2022, to December 31, 2023, for reviews which implemented RQS. Identification articles prior 2022 via previously published review. scores individual radiomics papers, their associated criteria scores, these all readers were extracted. Errors in RQS noted corrected. The papers matched with publication date, imaging modality, country, where available. Results total 130 included, quality 117/130 (90.0%), 98/130 (75.4%), multiple reader data 24/130 (18.5%) 3258 correlated study date publication. Criteria scoring errors discovered 39/98 (39.8%) articles. Overall mean 9.4 ± 6.4 (95% CI, 9.1–9.6) (26.1% 17.8% (25.3%–26.7%)). positively year (Pearson R = 0.32, p < 0.01) significantly higher after (year 2018, 5.6 6.1 (5.1–6.1); ≥ 10.1 (9.9–10.4); 0.01). Only 233/3258 (7.2%) 50% maximum different across modalities ( Ten year, one negatively correlated. Conclusion adherence is increasing time, although vast majority studies are developmental rarely provide high level evidence justify clinical translation proposed models. Key Points Question What have achieved has it increased sufficient? Findings extracted resulted score 6.4. time. Clinical relevance Although many not demonstrated sufficient translation. As new appraisal tools emerge, current role may change.

Язык: Английский

---

High-risk habitat radiomics model based on ultrasound images for predicting lateral neck lymph node metastasis in differentiated thyroid cancer

BMC Medical Imaging, Год журнала: 2025, Номер 25(1)

Опубликована: Янв. 13, 2025

This study aims to evaluate the predictive usefulness of a habitat radiomics model based on ultrasound images for anticipating lateral neck lymph node metastasis (LLNM) in differentiated thyroid cancer (DTC), and pinpointing high-risk regions significant traits. A group 214 patients diagnosed with carcinoma (DTC) between August 2021 2023 were included, consisting 107 confirmed postoperative without or cervical involvement. An additional cohort 43 was recruited serve as an independent external testing this study. Patients randomly divided into training internal at 8:2 ratio. Region interest (ROI) manually outlined, analysis subregions defined using K-means method. The ideal number (n = 5) determined Calinski-Harabasz score, leading creation 5 identification model. Area under curve (AUC) values calculated all models assess their validity, nomograms created by integrating clinical features. dataset is employed performance stability In group, Habitat 3 identified study, showing best diagnostic efficacy among (AUC(CRM) vs. AUC(Habitat 3) AUC(CRM + 0.84(95%CI:0.71–0.97) 0.90(95%CI:0.80-1.00) 0.79(95%CI:0.65–0.93)). Moreover, features constructing enhanced capability combined (AUC 0.95(95%CI:0.88-1.00)). utilized model's accuracy, yielding AUC 0.88 (95%CI: 0.78–0.98). integration High-Risk Habitats (Habitat characteristics demonstrated high accuracy identifying LLNM. has potential offer valuable guidance surgeons deciding necessity LLNM dissection DTC. Not applicable.

Язык: Английский

---