An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

Fusco, Roberta; Granata, Vincenza; Simonetti, Igino; Setola, Sergio Venanzio; Iasevoli, Maria Assunta Daniela; Tovecci, Filippo; Lamanna, Ciro Michele Paolo; Izzo, Francesco; Pecori, Biagio; Petrillo, Antonella

doi:10.3390/curroncol31010027

Cited by 8 publications

(1 citation statement)

References 105 publications

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“…Radiomics, a recent branch of science, can aid radiologists in making highly accurate diagnoses through deep mining, prediction, and analysis of large data sets 16 . Deep learning (DL), in contrast, has made significant strides in the field of breast imaging diagnosis, particularly in the detection and classification of breast cancer 17 .…”

Section: Introductionmentioning

confidence: 99%

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Liang,

Li,

Yao

et al. 2024

Sci Rep

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To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions.

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Section: Introductionmentioning

confidence: 99%

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Liang,

Li,

Yao

et al. 2024

Sci Rep

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Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact

Ferrari,

Trinci,

Casinelli

et al. 2024

Radiol med

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Cystic pancreatic neoplasms: what we need to know and new perspectives

Galluzzo,

Bogani,

Fedeli

et al. 2024

J Med Imaging Intervent Radiol

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Pancreatic cystic lesions (PCLs) include a broad spectrum of entities, which greatly differ in their malignant potential and may provide a diagnostic challenge. PCLs can be categorised into: pseudocysts; common cystic neoplasms; uncommon cystic neoplasms and tumours with cystic degeneration. Large lesions are usually possible to differentiate, but small lesions’ characterisation is often not possible. This means that many pancreatic cysts remain undetermined and guidelines are needed for follow-up and management. It is important to make distinction between mucinous cystic neoplasms (MCN) and intraductal pancreatic mucinous neoplasms (IPMN), because all MCN should be resected whereas there is time for observation in specific cases of side branch IPMN. With regards to IPMN, guidelines have established features associated with increased risk of malignancy: the “worrisome” features and high-risk stigmata. It should be considered that the presence of an IPMN may portend an increased risk of invasive pancreatic cancer in a different site of the pancreas. The assessment of PCLs should provide imaging features such as cyst morphology or presence of duct communication, and clinical findings including cystic fluid analysis to improve diagnostic accuracy. Radiomics represents an emerging field of interest that could add some information in the future for the differential diagnosis and follow-up of these lesions. The aim of this study was to define a diagnostic approach to PCLs according to the latest guidelines; a review of the latest developments in radiomics regarding PCLs was also carried out.

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An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

Cited by 8 publications

References 105 publications

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact

Cystic pancreatic neoplasms: what we need to know and new perspectives

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