MRI‐detected extramural vascular invasion (mrEMVI) as the best predictive factor to identify candidates to total neoadjuvant therapy in locally advanced rectal cancer

Massucco, Paolo; Fontana, A.; Mussetto, Annalisa Balbo; Desana, Benedetta; Ricotti, Andrea; Gonella, Federica; Mineccia, Michela; Cirillo, Stefano; Ferrero, Alessandro

doi:10.1002/jso.26818

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…RC may present numerous histopathological features, such as differentiation degree, EMVI, TB, LVI, and PNI that are linked with poor prognosis and have to be taken into account for stratifying the risk of RC patients [5]. A reliable pre-treatment assessment of these factors would probably favour and accelerate the progress of tailored treatment strategies and consequently, the transition toward precision medicine [5,[77][78][79].…”

Section: Assessing High-risk Histopathological Featuresmentioning

confidence: 99%

Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review

Costanzo

Ascione

Ponsiglione

et al. 2023

Exploration of Targeted Anti-Tumor Therapy

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Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.

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Section: Assessing High-risk Histopathological Featuresmentioning

confidence: 99%

Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review

Costanzo

Ascione

Ponsiglione

et al. 2023

Exploration of Targeted Anti-Tumor Therapy

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“…Several RC characteristics may impact recurrence and metastasis, including EMVI, differentiation degree, lymphatic vascular infiltration (LVI), perineural invasion (PNI) and tumor budding (TB), which have been considered for risk stratification 4 . It could be hypothesized that preoperative prediction of these high‐risk factors would help in the development of individualized treatment plans 21–23 …”

Section: Clinical Applications Of Ai In Rc Based On Mrimentioning

confidence: 99%

“…4 It could be hypothesized that preoperative prediction of these high-risk factors would help in the development of individualized treatment plans. [21][22][23] MRI is recommended to evaluate EMVI status; however, the sensitivity of conventional MRI in the evaluation of EMVI is still not ideal. 24 To improve the accuracy of EMVI in RC, Shu et al 25 constructed a joint model to predict EMVI based on T2WI, T1WI, DWI, and contrast-enhanced T1WI (CE-T1WI).…”

Section: High-risk Factorsmentioning

confidence: 99%

MRI‐Based Artificial Intelligence in Rectal Cancer

Wong

et al. 2022

Magnetic Resonance Imaging

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Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future.

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“…This feature is a stronger predictor of distant metastasis than other morphologic features of the tumor observed by MRI. Preoperative EMVI positivity in localized advanced rectal cancer also significantly increases the risk of recurrence and metastasis after radical surgery ( 3 , 4 ). The National Comprehensive Cancer Network (NCCN) and the European Society of Medical Oncology (ESMO) include baseline EMVI status as a risk stratification factor in patients with rectal cancer ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

Extramural vascular invasion nomogram before radical resection of rectal cancer based on magnetic resonance imaging

Tian

Li²,

Xie³

et al. 2023

Front. Oncol.

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PurposeThis study verified the value of magnetic resonance imaging (MRI) to construct a nomogram to preoperatively predict extramural vascular invasion (EMVI) in rectal cancer using MRI characteristics.Materials and methodsThere were 55 rectal cancer patients with EMVI and 49 without EMVI in the internal training group. The external validation group consisted of 54 rectal cancer patients with EMVI and 55 without EMVI. High-resolution rectal T2WI, pelvic diffusion-weighted imaging (DWI) sequences, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were used. We collected the following data: distance between the lower tumor margin and the anal margin, distance between the lower tumor margin and the anorectal ring, tumor proportion of intestinal wall, mrT stage, maximum tumor diameter, circumferential resection margin, superior rectal vein width, apparent diffusion coefficient (ADC), T2WI EMVI score, DWI and DCE-MRI EMVI scores, demographic information, and preoperative serum tumor marker data. Logistic regression analyses were used to identify independent risk factors of EMVI. A nomogram prediction model was constructed. Receiver operating characteristic curve analysis verified the predictive ability of the nomogram. P < 0.05 was considered significant.ResultTumor proportion of intestinal wall, superior rectal vein width, T2WI EMVI score, and carbohydrate antigen 19-9 were significant independent predictors of EMVI in rectal cancer and were used to create the model. The areas under the receiver operating characteristic curve, sensitivities, and specificities of the nomogram were 0.746, 65.45%, and 83.67% for the internal training group, respectively, and 0.780, 77.1%, and 71.3% for the external validation group, respectively.Data conclusionA nomogram including MRI characteristics can predict EMVI in rectal cancer preoperatively and provides a valuable reference to formulate individualized treatment plans and predict prognosis.

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MRI‐detected extramural vascular invasion (mrEMVI) as the best predictive factor to identify candidates to total neoadjuvant therapy in locally advanced rectal cancer

Cited by 6 publications

References 38 publications

Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review

Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review

MRI‐Based Artificial Intelligence in Rectal Cancer

Extramural vascular invasion nomogram before radical resection of rectal cancer based on magnetic resonance imaging

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