Radiomics MRI for lymph node status prediction in breast cancer patients: the state of art

Calabrese, Alessandro; Santucci, Domiziana; Landi, Roberta; Zobel, Bruno Beomonte; Faiella, Eliodoro; Felice, Carlo de

doi:10.1007/s00432-021-03606-6

Cited by 42 publications

(30 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also interesting to note that only some semantic features survived to the features selection process, such as the DCE-Kinetic Curve, which represents the tumor intrinsic vascularization, closely connected with the lesion aggressiveness and edema, as strongly reported in literature [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. However, for all the histological labels, the majority of selected features were radiomics, and in particular second-order ones.…”

Section: Discussionmentioning

confidence: 94%

“…In BC, radiomics is applied as a decision supporting tool, potentially improving lesion characterization and patient prognosis, mostly using MRI [ 19 ]. In recent literature several studies have evaluated the correlation between tumor features, extracted from MRI, and breast cancer molecular subtypes, with promising results [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. However, to our knowledge, there are no articles associating tumor edema radiomics with primary tumor imaging information in order to predict breast cancer biological aggressiveness.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study

Santucci

Faiella

Cordelli

et al. 2021

Cancers

Self Cite

View full text Add to dashboard Cite

Background: to evaluate the contribution of edema associated with histological features to the prediction of breast cancer (BC) prognosis using T2-weighted MRI radiomics. Methods: 160 patients who underwent staging 3T-MRI from January 2015 to January 2019, with 164 histologically proven invasive BC lesions, were retrospectively reviewed. Patient data (age, menopausal status, family history, hormone therapy), tumor MRI-features (location, margins, enhancement) and histological features (histological type, grading, ER, PgR, HER2, Ki-67 index) were collected. Of the 160 MRI exams, 120 were considered eligible, corresponding to 127 lesions. T2-MRI were used to identify edema, which was classified in four groups: peritumoral, pre-pectoral, subcutaneous, or diffuse. A semi-automatic segmentation of the edema was performed for each lesion, using 3D Slicer open-source software. Main radiomics features were extracted and selected using a wrapper selection method. A Random Forest type classifier was trained to measure the performance of predicting histological factors using semantic features (patient data and MRI features) alone and semantic features associated with edema radiomics features. Results: edema was absent in 37 lesions and present in 127 (62 peritumoral, 26 pre-pectoral, 16 subcutaneous, 23 diffuse). The AUC-classifier obtained by associating edema radiomics with semantic features was always higher compared to the AUC-classifier obtained from semantic features alone, for all five histological classes prediction (0.645 vs. 0.520 for histological type, 0.789 vs. 0.590 for grading, 0.487 vs. 0.466 for ER, 0.659 vs. 0.546 for PgR, and 0.62 vs. 0.573 for Ki67). Conclusions: radiomic features extracted from tumor edema contribute significantly to predicting tumor histology, increasing the accuracy obtained from the combination of patient clinical characteristics and breast imaging data.

show abstract

Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study

Santucci

Faiella

Cordelli

et al. 2021

Cancers

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lymph node size is not a predictor of involvement [ 18 ]. Even for breast cancers, the state-of-the-art for lymph node staging remains invasive with considerable morbidity; however, radiomics approaches analyzing characteristics of the primary tumor to predict lymph node status are an active area of research [ 49 ]. With pancreatic cancers and other solid tumor types, staging is possible only after lymph node dissection [ 50 ].…”

Section: Prior To Treatmentmentioning

confidence: 99%

“…While it may be impossible to find low-cost surrogates for non-invasive tracking of metabolic changes for treatment steering, it is conceivable that a diverse array of subtypes of hepatocellular carcinomas and pancreatic ductal adenocarcinomas can be uniquely identified and paired with effective treatment protocols. The aforementioned work predicting lymph node involvement based on primary tumor features is a hopeful example of the power of texture analysis feature extraction [ 49 ].…”

Section: Perspectivementioning

confidence: 99%

Emerging Role for 7T MRI and Metabolic Imaging for Pancreatic and Liver Cancer

Rivera

2022

Metabolites

View full text Add to dashboard Cite

Advances in magnet technologies have led to next generation 7T magnetic resonance scanners which can fit in the footprint and price point of conventional hospital scanners (1.5–3T). It is therefore worth asking if there is a role for 7T magnetic resonance imaging and spectroscopy for the treatment of solid tumor cancers. Herein, we survey the medical literature to evaluate the unmet clinical needs for patients with pancreatic and hepatic cancer, and the potential of ultra-high field proton imaging and phosphorus spectroscopy to fulfil those needs. We draw on clinical literature, preclinical data, nuclear magnetic resonance spectroscopic data of human derived samples, and the efforts to date with 7T imaging and phosphorus spectroscopy. At 7T, the imaging capabilities approach histological resolution. The spectral and spatial resolution enhancements at high field for phospholipid spectroscopy have the potential to reduce the number of exploratory surgeries due to tumor boundaries undefined at conventional field strengths. Phosphorus metabolic imaging at 7T magnetic field strength, is already a mainstay in preclinical models for molecular phenotyping, energetic status evaluation, dosimetry, and assessing treatment response for both pancreatic and liver cancers. Metabolic imaging of primary tumors and lymph nodes may provide powerful metrics to aid staging and treatment response. As tumor tissues contain extreme levels of phospholipid metabolites compared to the background signal, even spectroscopic volumes containing less than 50% tumor can be detected and/or monitored. Phosphorus spectroscopy allows non-invasive pH measurements, indicating hypoxia, as a predictor of patients likely to recur. We conclude that 7T multiparametric approaches that include metabolic imaging with phosphorus spectroscopy have the potential to meet the unmet needs of non-invasive location-specific treatment monitoring, lymph node staging, and the reduction in unnecessary surgeries for patients undergoing resections for pancreatic cancer. There is also potential for the use of 7T phosphorous spectra for the phenotyping of tumor subtypes and even early diagnosis (<2 mL). Whether or not 7T can be used for all patients within the next decade, the technology is likely to speed up the translation of new therapeutics.

show abstract

“…T2-weighted imaging (T2WI) and the dynamic enhanced portal venous phase (PVP) of MRI can better depict the biological characteristics of pancreatic lesions and have therefore been applied to radiomics studies of pancreatic cancer (18)(19)(20), including for differential diagnosis, prognosis evaluations, and treatment response predictions. Although studies have shown that radiomics can be used for the preoperative prediction of LNM of malignant tumors (21)(22)(23)(24), few radiomics studies based on MRI image texture analysis have been conducted for the preoperative prediction of LNM of PDAC. Therefore, this study aimed to explore whether the use of T2WI and PVP features was feasible for predicting LNM of PDAC.…”

mentioning

confidence: 99%

Preoperative Prediction of Lymph Node Metastasis of Pancreatic Ductal Adenocarcinoma Based on a Radiomics Nomogram of Dual-Parametric MRI Imaging

Shi

Wei³

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

PurposeThis study aims to uncover and validate an MRI-based radiomics nomogram for detecting lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) patients prior to surgery.Materials and MethodsWe retrospectively collected 141 patients with pathologically confirmed PDAC who underwent preoperative T2-weighted imaging (T2WI) and portal venous phase (PVP) contrast-enhanced T1-weighted imaging (T1WI) scans between January 2017 and December 2021. The patients were randomly divided into training (n = 98) and validation (n = 43) cohorts at a ratio of 7:3. For each sequence, 1037 radiomics features were extracted and analyzed. After applying the gradient-boosting decision tree (GBDT), the key MRI radiomics features were selected. Three radiomics scores (rad-score 1 for PVP, rad-score 2 for T2WI, and rad-score 3 for T2WI combined with PVP) were calculated. Rad-score 3 and clinical independent risk factors were combined to construct a nomogram for the prediction of LNM of PDAC by multivariable logistic regression analysis. The predictive performances of the rad-scores and the nomogram were assessed by the area under the operating characteristic curve (AUC), and the clinical utility of the radiomics nomogram was assessed by decision curve analysis (DCA).ResultsSix radiomics features of T2WI, eight radiomics features of PVP and ten radiomics features of T2WI combined with PVP were found to be associated with LNM. Multivariate logistic regression analysis showed that rad-score 3 and MRI-reported LN status were independent predictors. In the training and validation cohorts, the AUCs of rad-score 1, rad-score 2 and rad-score 3 were 0.769 and 0.751, 0.807 and 0.784, and 0.834 and 0.807, respectively. The predictive value of rad-score 3 was similar to that of rad-score 1 and rad-score 2 in both the training and validation cohorts (P > 0.05). The radiomics nomogram constructed by rad-score 3 and MRI-reported LN status showed encouraging clinical benefit, with an AUC of 0.845 for the training cohort and 0.816 for the validation cohort.ConclusionsThe radiomics nomogram derived from the rad-score based on MRI features and MRI-reported lymph status showed outstanding performance for the preoperative prediction of LNM of PDAC.

show abstract

Radiomics MRI for lymph node status prediction in breast cancer patients: the state of art

Cited by 42 publications

References 32 publications

The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study

The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study

Emerging Role for 7T MRI and Metabolic Imaging for Pancreatic and Liver Cancer

Preoperative Prediction of Lymph Node Metastasis of Pancreatic Ductal Adenocarcinoma Based on a Radiomics Nomogram of Dual-Parametric MRI Imaging

Contact Info

Product

Resources

About