Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis?Eur Radiol. 2019;29:6469-6476. [PMID: 31278581 DOI: 10.1007/s00330-019-06328-z]

A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer

Objective

Accurate preoperative evaluation of rectal cancer lung metastases (RCLM) is critical for implementing precise medicine. While artificial intelligence (AI) methods have been successful in detecting liver and lymph node metastases using magnetic resonance (MR) images, research on lung metastases is still limited. Utilizing MR images to classify RCLM could potentially reduce ionizing radiation exposure and the costs associated with chest CT in patients without metastases. This study aims to develop and validate a transformer-based deep learning (DL) model based on pelvic MR images, integrated with clinical features, to predict RCLM.

Methods

A total of 819 patients with histologically confirmed rectal cancer who underwent preoperative pelvis MRI and carcinoembryonic antigen (CEA) tests were enrolled. Six state-of-the-art DL methods (Resnet18, EfficientNetb0, MobileNet, ShuffleNet, DenseNet, and our transformer-based model) were trained and tested on T2WI and DWI to predict RCLM. The predictive performance was assessed using the receiver operating characteristic (ROC) curve.

Results

Our transformer-based DL model achieved impressive results in the independent test set, with an AUC of 83.74% (95% CI, 72.60%-92.83%), a sensitivity of 80.00%, a specificity of 78.79%, and an accuracy of 79.01%. Specifically, for stage T4 and N2 rectal cancer cases, the model achieved AUCs of 96.67% (95% CI, 87.14%-100%, 93.33% sensitivity, 89.04% specificity, 94.74% accuracy), and 96.83% (95% CI, 88.67%-100%, 100% sensitivity, 83.33% specificity, 88.00% accuracy) respectively, in predicting RCLM. Our DL model showed a better predictive performance than other state-of-the-art DL methods.

Conclusion

The superior performance demonstrates the potential of our work for predicting RCLM, suggesting its potential assistance in personalized treatment and follow-up plans.

Assessment of prognostic indicators and KRAS mutations in rectal cancer using a fractional-order calculus MR diffusion model: whole tumor histogram analysis

PURPOSE

This study aims to explore the relationship between apparent diffusion coefficient (ADC) and fractional-order calculus (FROC)-specific parameters with prognostic indicators and Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation status in rectal cancer.

METHODS

One hundred fifty-eight patients with rectal cancer were retrospectively enrolled. Histogram measurements of ADC, diffusion coefficient (D), intravoxel diffusion heterogeneity (β), and a microstructural quantity (μ) were estimated for the whole-tumor volume. The relationships between histogram measurements and prognostic indicators were evaluated. The efficacy of histogram measurements, both conducted singly and in conjunction, for evaluating different KRAS mutation statuses was also assessed. The performance of mean and median histogram measurements in evaluating various KRAS mutation statuses was assessed using Receiver Operating Characteristic (ROC) curve analysis. A p-value of less than 0.05 was considered statistically significant.

RESULTS

The histogram measurements of ADC, D, β, and μ differed significantly between well-moderately differentiated groups and poorly differentiated groups, T1-2 and T3-4 subgroups, lymph node metastasis (LNM)-negative and LNM-positive subgroups, extranodal extension (ENE)-negative and ENE-positive subgroups, tumor deposit (TD)-negative and TD-positive subgroups, and lymphovascular invasion (LVI)-negative and LVI-positive subgroups. The combination of Dmean, βmean, and μmean achieved the highest performance [The area under the ROC curve (AUC) = 0.904] in evaluating the KRAS mutation status.

CONCLUSION

When assessing parameters from the FROC model as potential biomarkers through histograms, they surpass traditional ADC values in distinguishing prognostic indicators and determining KRAS mutation status in rectal cancer.

Pathological prognostic factors of rectal cancer based on diffusion-weighted imaging, intravoxel incoherent motion, and diffusion kurtosis imaging

OBJECTIVES

To explore diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for assessing pathological prognostic factors in patients with rectal cancer.

MATERIALS AND METHODS

A total of 162 patients (105 males; mean age of 61.8 ± 13.1 years old) scheduled to undergo radical surgery were enrolled in this prospective study. The pathological prognostic factors included histological differentiation, lymph node metastasis (LNM), and extramural vascular invasion (EMVI). The DWI, IVIM, and DKI parameters were obtained and correlated with prognostic factors using univariable and multivariable logistic regression. Their assessment value was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Multivariable logistic regression analyses showed that higher mean kurtosis (MK) (odds ratio (OR) = 194.931, p < 0.001) and lower apparent diffusion coefficient (ADC) (OR = 0.077, p = 0.025) were independently associated with poorer differentiation tumors. Higher perfusion fraction (f) (OR = 575.707, p = 0.023) and higher MK (OR = 173.559, p < 0.001) were independently associated with LNMs. Higher f (OR = 1036.116, p = 0.024), higher MK (OR = 253.629, p < 0.001), lower mean diffusivity (MD) (OR = 0.125, p = 0.038), and lower ADC (OR = 0.094, p = 0.022) were independently associated with EMVI. The area under the ROC curve (AUC) of MK for histological differentiation was significantly higher than ADC (0.771 vs. 0.638, p = 0.035). The AUC of MK for LNM positivity was higher than f (0.770 vs. 0.656, p = 0.048). The AUC of MK combined with MD (0.790) was the highest among f (0.663), MK (0.779), MD (0.617), and ADC (0.610) in assessing EMVI.

CONCLUSION

The DKI parameters may be used as imaging biomarkers to assess pathological prognostic factors of rectal cancer before surgery.

CLINICAL RELEVANCE STATEMENT

Diffusion kurtosis imaging (DKI) parameters, particularly mean kurtosis (MK), are promising biomarkers for assessing histological differentiation, lymph node metastasis, and extramural vascular invasion of rectal cancer. These findings suggest DKI's potential in the preoperative assessment of rectal cancer.

KEY POINTS

Mean kurtosis outperformed the apparent diffusion coefficient in assessing histological differentiation in resectable rectal cancer. Perfusion fraction and mean kurtosis are independent indicators for assessing lymph node metastasis in rectal cancer. Mean kurtosis and mean diffusivity demonstrated superior accuracy in assessing extramural vascular invasion.

Radiomics in rectal cancer: current status of use and advances in research

Rectal cancer is a leading cause of morbidity and mortality among patients with malignant tumors in China. In light of the advances made in therapeutic approaches such as neoadjuvant therapy and total mesorectal excision, precise preoperative assessment has become crucial for developing a personalized treatment plan. As an emerging technology, radiomics has gained widespread application in the diagnosis, assessment of treatment response, and analysis of prognosis for rectal cancer by extracting high-throughput quantitative features from medical images. Radiomics thus demonstrates considerable potential for optimizing clinical decision-making. In this paper, we reviewed recent research focusing on advances in the use of radiomics for managing rectal cancer. The review covers TNM staging of tumors, assessment of neoadjuvant therapy outcomes, and survival prediction. We also discuss the challenges and prospects for future developments in translational medicine, particularly the need for data standardization, consistent feature extraction methodologies, and rigorous model validation.

Development of a nomogram based on whole-tumor multiparametric MRI histogram analysis to predict deep myometrial invasion in stage I endometrioid endometrial carcinoma preoperatively

BACKGROUND

The depth of myometrial invasion determines whether International Federation of Gynecology and Obstetrics stage I endometrioid endometrial carcinoma (EEC) patients undergo lymph node dissection. However, subjective evaluation results relying on magnetic resonance imaging (MRI) are not always satisfactory.

PURPOSE

To develop a nomogram based on whole-volume tumor MRI histogram parameters to preoperatively predict deep myometrial invasion (DMI) in patients with stage I EEC.

MATERIAL AND METHODS

This retrospective analysis included 131 EEC patients and a training/validation cohort of 92/39 patients at a 7:3 ratio. The histogram parameters were obtained from multiple sequences (ADC mapping and T2-weighted imaging) within volumes of interest. Univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression were used for feature selection. The performance of clinical model, histogram model, and histogram nomogram was evaluated by calculating the area under the receiver operating characteristic curve (AUC).

RESULTS

Age and two morphological features (maximum anteroposterior tumor diameter on sagittal T2-weighted images [APsag] and the tumor area ratio [TAR]) were selected to construct the clinical model. Five histogram parameters were selected for the creation of the histogram model. The nomogram, which combines the histogram parameters, age, APsag, and TAR, achieved the highest AUCs in both the training and validation cohorts (nomogram vs. histogram vs. clinical model: 0.973 vs. 0.871 vs. 0.934 [training] and 0.972 vs. 0.870 vs. 0.928 [validation]).

CONCLUSION

The MR histogram nomogram can help predict the DMI of patients with stage I EEC preoperatively, assisting physicians in the development of personalized treatment strategies.

Fractional order calculus model-derived histogram metrics for assessing pathological complete response to neoadjuvant chemotherapy in locally advanced rectal cancer

AIM

This study evaluates the value of diffusion fractional order calculus (FROC) model for the assessment of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC) by using histogram analysis derived from whole-tumor volumes.

MATERIALS AND METHODS

Ninety-eight patients were prospectively included. Every patient received MRI scans before and after nCRT using a 3.0-Tesla MRI machine. Parameters of the FROC model, including the anomalous diffusion coefficient (D), intravoxel diffusion heterogeneity (β), spatial parameter (μ), and the standard apparent diffusion coefficient (ADC), were calculated. Changes in median values (ΔX-median) and ratio (rΔX-median) were calculated. Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance.

RESULTS

Pre-treatmentβ-10th percentile values were significantly lower in the pCR group compared to the non-pCR group (p < 0.001). The Δβ-median showed higher diagnostic accuracy (AUC = 0.870) and sensitivity (76.67 %) for predicting tumor response compared to MRI tumor regression grading (mrTRG) scores (AUC = 0.722; sensitivity = 90.0 %).

DISCUSSION

The use of FROC alongside comprehensive tumor histogram analysis was found to be practical and effective in evaluating the tumor response to nCRT in LARC patients.

Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study

OBJECTIVES

To explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis.

METHODS

A total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed. Twelve radiomics models were constructed using Lasso-Logistic or Lasso-Cox to evaluate and four annotation methods, 2D detailed annotation along tumour boundaries (2D), 3D detailed annotation along tumour boundaries (3D), 2D bounding box (2DBB), and 3D bounding box (3DBB) on T2-weighted images, were compared. Radiomics models were used to establish combined models incorporating clinical risk factors. The DeLong test was performed to compare the performance of models using the receiver operating characteristic curves.

RESULTS

For radiomics models, the area under the curve values ranged from 0.627 (0.518-0.728) to 0.811 (0.705-0.917) in the internal validation cohort and from 0.619 (0.469-0.754) to 0.824 (0.689-0.918) in the external validation cohort. Most radiomics models based on four annotations did not differ significantly, except between the 3D and 3DBB models for pLNM (p = 0.0188) in the internal validation cohort. For combined models, only the 2D model significantly differed from the 2DBB (p = 0.0372) and 3D models (p = 0.0380) for pDFS.

CONCLUSION

Radiomics and combined models constructed with 2D and bounding box annotations showed comparable performances to those with 3D and detailed annotations along tumour boundaries in rectal cancer characterisation and prognosis prediction.

CRITICAL RELEVANCE STATEMENT

For quantitative analysis of radiological images, the selection of 2D maximum tumour area or bounding box annotation is as representative and easy to operate as 3D whole tumour or detailed annotations along tumour boundaries.

KEY POINTS

There is currently a lack of discussion on whether different annotation efforts in radiomics are predictively representative. No significant differences were observed in radiomics and combined models regardless of the annotations (2D, 3D, detailed, or bounding box). Prioritise selecting the more time and effort-saving 2D maximum area bounding box annotation.

Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer

AIM

Preoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer.

MATERIALS AND METHODS

A total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n ​= ​159) and a validation set (n ​= ​40). The total incidence of TDs was 37.2 ​% (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI ​+ ​ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores.

RESULTS

The Rad-score (T2WI ​+ ​ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI ​+ ​ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 ​% in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 ​%, 92.0 ​%, 83.3 ​%, and 82.1 ​%, respectively.

CONCLUSION

The radiomics nomogram based on Rad-score (T2WI ​+ ​ADC) and clinical factors provides a promising and effective method for the preoperative prediction of TDs in patients with rectal cancer.

Clinical development of MRI-based multi-sequence multi-regional radiomics model to predict lymph node metastasis in rectal cancer

OBJECTIVE

We aim to construct a magnetic resonance imaging (MRI)-based multi-sequence multi-regional radiomics model that will improve the preoperative prediction ability of lymph node metastasis (LNM) in T3 rectal cancer.

METHODS

Multi-sequence MRI data from 190 patients with T3 rectal cancer were retrospectively analyzed, with 94 patients in the LNM group and 96 patients in the non-LNM group. The clinical factors, subjective imaging features, and the radiomic features of tumor and peritumoral mesorectum region of patients were extracted from T2WI and ADC images. Spearman's rank correlation coefficient, Mann-Whitney's U test, and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Logistic regression was used to construct six models. The predictive performance of each model was evaluated by the receiver operating characteristic curve (ROC). The differences of each model were characterized by area under the curve (AUC) via the DeLong test.

RESULTS

The AUCs of T2WI, ADC single-sequence radiomics model and multi-sequence radiomics model were 0.73, 0.75, and 0.78, respectively. The multi-sequence multi-regional radiomics model with improved performance was created by combining the radiomics characteristics of the peritumoral mesorectum region with the multi-sequence radiomics model (AUC, 0.87; p < 0.01). The AUC of the clinical model was 0.68, and the MRI-clinical composite evaluation model was obtained by incorporating the clinical data with the multi-sequence multi-regional radiomics features, with an AUC of 0.89.

CONCLUSION

The MRI-based multi-sequence multi-regional radiomics model significantly improved the prediction ability of LNM for T3 rectal cancer and could be applied to guide surgical decision-making in patients with T3 rectal cancer.

A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer

PURPOSE

To develop and validate a nomogram for the preoperative diagnosis of T2 and T3 stage rectal cancer using MRI radiomics features of mesorectal fat.

METHODS

The data of 288 patients with T2 and T3 stage rectal cancer were retrospectively collected. Radiomics features were extracted from the lesion region of interest (ROI) in the MRI high-resolution T2WI, apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) sequences. After using ICC inter-group consistency analysis and Pearson correlation analysis to reduce dimensions, LASSO regression analysis was performed to select features and calculate Rad-score for each sequence. Then, Combined_Radscore and nomogram were constructed based on the LASSO-selected features and clinical data for each sequence. Receiver operating characteristic curve (ROC) area under the curve (AUC) was used to evaluate the performance of the Rad-score model and nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical usability of the radiomics nomogram, which were combined with calibration curves to evaluate the prediction accuracy.

RESULTS

The nomogram based on MRI-report T status and Combined_Radscore achieved AUCs of 0.921 and 0.889 in the training and validation cohorts, respectively.

CONCLUSION

The nomogram can be stated that the radiomics nomogram based on multi-sequence MRI imaging of the mesorectal fat has excellent diagnosing performance for preoperative differentiation of T2 and T3 stage rectal cancer.

T2WI-based texture analysis predicts preoperative lymph node metastasis of rectal cancer

BACKGROUND

To prospectively develop and validate the T2WI texture analysis model based on a node-by-node comparison for improving the diagnostic accuracy of lymph node metastasis (LNM) in rectal cancer.

METHODS

A total of 381 histopathologically confirmed lymph nodes (LNs) were collected. LNs texture features were extracted from MRI-T2WI. Spearman's rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection to construct the LN rad-score. Then the clinical risk factors and LN texture features were combined to establish combined predictive model. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Decision curve analysis (DCA) and nomogram were used to evaluate the clinical application of the model.

RESULTS

A total of 107 texture features were extracted from LN-MRI images. After selection and dimensionality reduction, the radiomics prediction model consisting of 8 texture features showed well-predictive performance in the training and validation cohorts (AUC, 0.676; 95% CI 0.582-0.771) (AUC, 0.774; 95% CI 0.648-0.899). A clinical-radiomics prediction model with the best performance was created by combining clinical and radiomics features, 0.818 (95% CI 0.742-0.893) for the training and 0.922 (95% CI 0.863-0.980) for the validation cohort. The LN Rad-score in clinical-radiomics nomogram obtained the highest classification contribution and was well calibrated. DCA demonstrated the superiority of the clinical-radiomics model.

CONCLUSION

The lymph node T2WI-based texture features can help to improve the preoperative prediction of LNM.

Presurgical MRI-Based Radiomics Models for Predicting Cerebellar Mutism Syndrome in Children With Posterior Fossa Tumors

BACKGROUND

Current studies have indicated that tumoral morphologic features are associated with cerebellar mutism syndrome (CMS), but the radiomics application in CMS is scarce.

PURPOSE

To develop a model for CMS discrimination based on multiparametric MRI radiomics in patients with posterior fossa tumors.

STUDY TYPE

Retrospective.

POPULATION

A total of 218 patients (males 132, females 86) with posterior fossa tumors, 169 of which were included in the MRI radiomics analysis. The MRI radiomics study cohort (169) was split into training (119) and testing (50) sets with a ratio of 7:3.

FIELD/SEQUENCE

All the MRI were acquired under 1.5/3.0 T scanners. T2-weighted image (T2W), T1-weighted (T1W), fluid attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI).

ASSESSMENT

Apparent diffusion coefficient (ADC) maps were generated from DWI. Each MRI dataset generated 1561 radiomics characteristics. Feature selection was performed with univariable logistic analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO) penalized logistic regression. Significant clinical features were selected with multivariable logistic analysis and used to constructed the clinical model. Radiomics models (based on T1W, T2W, FLAIR, DWI, ADC) were constructed with selected radiomics features. The mix model was based on the multiparametric MRI radiomics features.

STATISTICAL TEST

Multivariable logistic analysis was utilized during clinical features selection. Models' performance was evaluated using the area under the receiver operating characteristic (AUC) curve. Interobserver variability was assessed using Cohen's kappa. Significant threshold was set as P < 0.05.

RESULTS

Sex (aOR = 3.72), tumor location (aOR = 2.81), hydrocephalus (aOR = 2.14), and tumor texture (aOR = 5.08) were significant features in the multivariable analysis and were used to construct the clinical model (AUC = 0.79); totally, 33 radiomics features were selected to construct radiomics models (AUC = 0.63-0.93). Seven of the 33 radiomics features were selected for the mix model (AUC = 0.93).

DATA CONCLUSION

Multiparametric MRI radiomics may be better at predicting CMS than single-parameter MRI models and clinical model.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

2.

Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer

Objective

To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural spread ≤ 5 mm).

Methods

This retrospective study enrolled 303 patients with low T-staging rectal cancer (training cohort, n = 213, testing cohort n = 90). A total of 960 radiomics features were extracted from T2WI. Minimum redundancy and maximum relevance (mRMR) and support vector machine were performed to select the best performed radiomics features for predicting LN metastasis. Multivariate logistic regression analysis was then used to construct the clinical and clinical-radiomics combined models. The model performance for predicting LN metastasis was assessed by receiver operator characteristic curve (ROC) and clinical utility implementing a nomogram and decision curve analysis (DCA). The predictive performance for LN metastasis was also compared between the combined model and human readers (2 seniors).

Results

Fourteen radiomics features and 2 clinical characteristics were selected for predicting LN metastasis. In the testing cohort, a higher positive predictive value of 75.9% for the combined model was achieved than those of the clinical model (44.8%) and two readers (reader 1: 54.9%, reader 2: 56.3%) in identifying LN metastasis. The interobserver agreement between 2 readers was moderate with a kappa value of 0.416. A clinical-radiomics nomogram and decision curve analysis demonstrated that the combined model was clinically useful.

Conclusion

T2WI-based radiomics combined with clinical data could improve the efficacy in noninvasively evaluating LN metastasis for the low T-staging rectal cancer and aid in tailoring treatment strategies.

Infliximab response associates with radiologic findings in bio-naïve Crohn's disease

OBJECTIVES

Since a reliable model for predicting infliximab (IFX) benefits in bio-naïve Crohn's disease (CD) is still lacking, we constructed a magnetic resonance enterography (MRE)-based model to predict the risk of loss of response to IFX in bio-naïve patients with CD.

METHODS

This retrospective multicenter study enrolled 188 bio-naïve patients with CD who underwent MRE before IFX therapy. Therapeutic outcomes were determined based on clinical symptoms and endoscopic findings within 52 weeks. The areas of bowel wall segmentation were decided by two experienced radiologists in consensus. Texture features were extracted using the least absolute shrinkage and selection operator, and a radiomic model was built using multivariate logistic regression. The model performance was validated by receiver operating characteristic, calibration curve, and decision curve analysis.

RESULTS

The area under the curve of radiomic model was 0.88 (95% confidence interval: 0.82-0.95), and the model provided clinical net benefit in identifying the loss of response to IFX and exhibited remarkable robustness among centers, scanners, and disease characteristics. The high-risk patients defined by the radiomic model were more likely to develop IFX nonresponse than low-risk patients (all p < 0.05).

CONCLUSIONS

This novel pretreatment MRE-based model could act as an effective tool for the early estimation of loss of response to IFX in bio-naïve patients with CD.

KEY POINTS

• Magnetic resonance enterography model guides infliximab therapy in Crohn's disease. • The model presented significant discrimination and provided net clinical benefit. • Model divided patients into low- and high-risk groups for infliximab failure.

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

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.

Radiomics in colorectal cancer patients

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.

MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer

PURPOSE

To investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: Eighty-three patients (training cohort, n = 57; validation cohort, n = 26) with LARC between June 2017 and December 2022 were retrospectively enrolled. All the radiomics features were extracted from volume of interest on T2WI and ADC images from baseline and post-nCRT MRI. Delta-radiomics features were defined as the difference between radiomics features before and after nCRT. Seven clinical-radiomics models were constructed by combining the most predictive radiomics signatures and clinical parameters selected from support vector machine. Receiver operating characteristic curve (ROC) was used to evaluate the performance of models. The optimum model-based LNM was applied to assess 5-years disease-free survival (DFS) using Kaplan-Meier analysis. The end point was clinical or radiological locoregional recurrence or distant metastasis during postoperative follow-up.

RESULTS

Clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training (AUC = 0.895,95%CI:0.838-0.953) and validation cohort (AUC = 0.900,95%CI:0.771-1.000). Clinical-deltaADC radiomics-postT2WI radiomics combined model also showed good performances (AUC = 0.913,95%CI:0.838-0.953) in the training and (AUC = 0.912,95%CI:0.771-1.000) validation cohort. As for subgroup analysis, clinical-deltaADC radiomics combined model showed good performance predicting LNM in ypT0-T2 (AUC = 0.827;95%CI:0.649-1.000) and ypT3-T4 stage (AUC = 0.934;95%CI:0.864-1.000). In ypT0-T2 stage, clinical-deltaADC radiomics combined model-based LNM could assess 5-years DFS (P = 0.030).

CONCLUSION

Clinical-deltaADC radiomics combined model could predict post-nCRT LNM, and this combined model-based LNM was associated with 5-years DFS in ypT0-T2 stage.

Development of an improved diagnostic nomogram for preoperative prediction of small cell neuroendocrine cancer of the cervix

OBJECTIVES

Accurate preoperative diagnosis of small cell neuroendocrine cancer of the cervix (SCNECC) is crucial for establishing the best treatment plan. This study aimed to develop an improved, non-invasive method for the preoperative diagnosis of SCNECC by integrating clinical, MR morphological, and apparent diffusion coefficient (ADC) information.

METHODS

A total of 105 pathologically confirmed cervical cancer patients (35 SCNECC, 70 non-SCNECC) from multiple centres with complete clinical and MR records were included. Whole lesion histogram analysis of the ADC was performed. Multivariate logistic regression analysis was used to develop diagnostic models based on clinical, morphological, and histogram data. The predictive performance in terms of discrimination, calibration, and clinical usefulness of the different models was assessed. A nomogram for preoperatively discriminating SCNECC was developed from the combined model.

RESULTS

In preoperative SCNECC diagnosis, the combined model, which had a diagnostic AUC (area under the curve) of 0.937 (95% CI: 0.887-0.987), outperformed the clinical-morphological model, which had an AUC of 0.869 (CI: 0.788-0.949), and the histogram model, which had an AUC of 0.872 (CI: 0.792-0.951). The calibration curve and decision curve analyses suggest that the combined model achieved good fitting and clinical utility.

CONCLUSIONS

Non-invasive preoperative diagnosis of SCNECC can be achieved with high accuracy by integrating clinical, MR morphological, and ADC histogram features. The nomogram derived from the combined model can provide an easy-to-use clinical preoperative diagnostic tool for SCNECC.

ADVANCES IN KNOWLEDGE

It is clear that the therapeutic strategies for SCNECC are different from those for other pathological types of cervical cancer according to V 1.2021 of the NCCN clinical practice guidelines in oncology for cervical cancer. This research developed an improved, non-invasive method for the preoperative diagnosis of SCNECC by integrating clinical, MR morphological, and apparent diffusion coefficient (ADC) information.

Synaptophysin-like 2 expression correlates with lymph node metastasis and poor prognosis in colorectal cancer patients

BACKGROUND

Colorectal cancer (CRC) is one of the most common and fatal cancers worldwide. Synaptophysin-like 2 (SYPL2) is a neuroendocrine-related protein highly expressed in skeletal muscle and the tongue. The involvement of SYPL2 in CRC, including its level of expression and function, has not been evaluated.

AIM

To evaluate the correlations of SYPL2 expression with lymph node metastasis (LNM) and prognosis in patients with CRC.

METHODS

The levels of expression of SYPL2 in CRC and normal colorectal tissues were analyzed in multiple public and online databases. The associations between clinical variables and SYPL2 expression were evaluated statistically, and the associations between SYPL2 expression and prognosis in patients with CRC were analyzed using the Kaplan-Meier method and univariate/multivariate Cox regression analyses. SYPL2 expression was assessed in 20 paired CRC tissue and adjacent normal colorectal tissue samples obtained from Fuyang People’s Hospital, and the associations between SYPL2 expression and the clinical characteristics of these patients were investigated. Correlations between the levels of expression of SYPL2 and key targeted genes were determined by Pearson’s correlation analysis. The distribution of immune cells in these samples was calculated using the CIBERSORT algorithm. Gene set enrichment analysis (GSEA) was performed to evaluate the biofunction and pathways of SYPL2 in CRC.

RESULTS

SYPL2 expression was significantly lower in CRC tissue samples than in normal colorectal tissue samples (P < 0.05). High SYPL2 levels in CRC tissues correlated significantly with LNM (P < 0.05) and a poorer patient prognosis, including significantly shorter overall survival (OS) [hazard ratio (HR) = 1.9, P < 0.05] and disease-free survival (HR = 1.6, P < 0.05). High SYPL2 expression was an independent risk factor for OS in both univariate (HR = 2.078, P = 0.014) and multivariate (HR = 1.754, P = 0.018) Cox regression analyses. In addition, SYPL2 expression correlated significantly with the expression of KDR (P < 0.0001, r = 0.47) and the BRAF^V600E mutation (P < 0.05). Higher SYPL2 expression was associated with the enrichment of CD8 T-cells and M0 macrophages in the tumor microenvironment. GSEA revealed that SYPL2 was associated with the regulation of epithelial cell migration, vasculature development, pathways in cancer, and several vital tumor-related pathways.

CONCLUSION

SYPL2 expression was lower in CRC tissue than in normal colorectal tissue. Higher SYPL2 expression in CRC was significantly associated with LNM and poorer survival.

Predicting perineural invasion using histogram analysis of zoomed EPI diffusion-weighted imaging in rectal cancer

PURPOSE

To investigate the utility of histogram analysis of zoomed EPI diffusion-weighted imaging (DWI) for predicting the perineural invasion (PNI) status of rectal cancer (RC).

METHODS

This prospective study evaluated 94 patients diagnosed with histopathologically confirmed RC between July 2020 and July 2021. Patients underwent preoperative rectal magnetic resonance imaging (MRI) examinations, including the zoomed EPI DWI sequence. Ten whole-tumor histogram parameters of each patient were derived from zoomed EPI DWI. Reproducibility was evaluated according to the intra-class correlation coefficient (ICC). The association of the clinico-radiological and histogram features with PNI status was assessed using univariable analysis for trend and multivariable logistic regression analysis with β value calculation. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance.

RESULTS

Forty-two patients exhibited positive PNI. The inter- and intraobserver agreements were excellent for the histogram parameters (all ICCs > 0.80). The maximum (p = 0.001), energy (p = 0.021), entropy (p = 0.021), kurtosis (p < 0.001), and skewness (p < 0.001) were significantly higher in the positive PNI group than in the negative PNI group. Multivariable analysis showed that higher MRI T stage [β = 2.154, 95% confidence interval (CI) 0.932-3.688; p = 0.002] and skewness (β = 0.779, 95% CI 0.255-1.382; p = 0.006) were associated with positive PNI. The model combining skewness and MRI T stage had an area under the ROC curve of 0.811 (95% CI 0.724-0.899) for predicting PNI status.

CONCLUSION

Histogram parameters in zoomed EPI DWI can help predict the PNI status in RC.

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

Purpose

This 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 Methods

We 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).

Results

Six 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 &gt; 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.

Conclusions

The 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.

18F-FDG-PET/MRI texture analysis in rectal cancer after neoadjuvant chemoradiotherapy

OBJECTIVE

Reliable markers to predict the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) are lacking. We aimed to assess the ability of 18F-FDG PET/MRI to predict response to nCRT among patients undergoing curative-intent surgery.

METHODS

Patients with histological-confirmed LARC who underwent curative-intent surgery following nCRT and restaging with 18F-FDG PET/MRI were included. Statistical correlation between radiomic features extracted in PET, apparent diffusion coefficient (ADC) and T2w images and patients' histopathologic response to chemoradiotherapy using a multivariable logistic regression model ROC-analysis.

RESULTS

Overall, 50 patients were included in the study. A pathological complete response was achieved in 28.0% of patients. Considering second-order textural features, nine parameters showed a statistically significant difference between the two groups in ADC images, six parameters in PET images and four parameters in T2w images. Combining all the features selected for the three techniques in the same multivariate ROC curve analysis, we obtained an area under ROC curve of 0.863 (95% CI, 0.760-0.966), showing a sensitivity, specificity and accuracy at the Youden's index of 100% (14/14), 64% (23/36) and 74% (37/50), respectively.

CONCLUSION

PET/MRI texture analysis seems to represent a valuable tool in the identification of rectal cancer patients with a complete pathological response to nCRT.

Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective

The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.

A Nomogram of Combining IVIM-DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively

BACKGROUND

Lymph node (LN) staging plays an important role in treatment decision-making. Current problem is that preoperative detection of LN involvement is always highly challenging for radiologists.

PURPOSE

To explore the value of the nomogram model combining intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and radiomics features from the primary lesion of rectal adenocarcinoma in assessing the non-enlarged lymph node metastasis (N-LNM) preoperatively.

STUDY TYPE

Retrospective.

POPULATION

A total of 126 patients (43% female) comprising a training group (n = 87) and a validation group (n = 39) with pathologically confirmed rectal adenocarcinoma.

FIELD STRENGTH/SEQUENCE

A 3.0 Tesla (T); T₂ -weighted imaging (T₂ WI) with fast spin-echo (FSE) sequence; IVIM-DWI spin-echo echo-planar imaging sequence.

ASSESSMENT

Based on pathological analysis of the surgical specimen, patients were classified into negative LN (LN-) and positive LN (LN+) groups. Apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*) and microvascular volume fraction (f) values of primary lesion of rectal adenocarcinoma were measured. Three-dimensional (3D) radiomics features were measured on T₂ WI and IVIM-DWI. A nomogram model including IVIM-DWI and radiomics features was developed.

STATISTICAL TESTS

General_univariate_analysis and multivariate logistic regression were used for radiomics features selection. The performance of the nomogram was assessed by the receiver operating characteristic (ROC) curve, calibration, and decision curve analysis (DCA).

RESULTS

The LN+ group had a significantly lower D* value ([13.20 ± 13.66 vs. 23.25 ± 18.71] × 10^-3  mm² /sec) and a higher f value (0.43 ± 0.12 vs. 0.34 ± 0.10) than the LN- group in the training cohort. The nomogram model combined D*, f, and radiomics features had a better evaluated performance (AUC = 0.864) than any other model in the training cohort.

DATE CONCLUSION

The nomogram model including IVIM-DWI and MRI radiomics features in the primary lesion of rectal adenocarcinoma was associated with the N-LNM.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

MRI-based radiomics and ADC values are related to recurrence of endometrial carcinoma: a preliminary analysis

Background

To identify predictive value of apparent diffusion coefficient (ADC) values and magnetic resonance imaging (MRI)-based radiomics for all recurrences in patients with endometrial carcinoma (EC).

Methods

One hundred and seventy-four EC patients who were treated with operation and followed up in our institution were retrospectively reviewed, and the patients were divided into training and test group. Baseline clinicopathological features and mean ADC (ADC_mean), minimum ADC (ADC_min), and maximum ADC (ADC_max) were analyzed. Radiomic parameters were extracted on T2 weighted images and screened by logistic regression, and then a radiomics signature was developed to calculate the radiomic score (radscore). In training group, Kaplan–Meier analysis was performed and a Cox regression model was used to evaluate the correlation between clinicopathological features, ADC values and radscore with recurrence, and verified in the test group.

Results

ADC_mean showed inverse correlation with recurrence, while radscore was positively associated with recurrence. In univariate analyses, FIGO stage, pathological types, myometrial invasion, ADC_mean, ADC_min and radscore were associated with recurrence. In the training group, multivariate Cox analysis showed that pathological types, ADC_mean and radscore were independent risk factors for recurrence, which were verified in the test group.

Conclusions

ADC_mean value and radscore were independent predictors of recurrence of EC, which can supplement prognostic information in addition to clinicopathological information and provide basis for individualized treatment and follow-up plan.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08988-x.

Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict LNM in T3 stage rectal carcinoma

BACKGROUND

Preoperative identification of rectal cancer lymph node status is crucial for patient prognosis and treatment decisions. Rectal magnetic resonance imaging (MRI) plays an essential role in the preoperative staging of rectal cancer, but its ability to predict lymph node metastasis (LNM) is insufficient. This study explored the value of histogram features of primary lesions on multi-parametric MRI for predicting LNM of stage T3 rectal carcinoma.

METHODS

We retrospectively analyzed 175 patients with stage T3 rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging (DWI) before surgery. 62 patients were included in the LNM group, and 113 patients were included in the non-LNM group. Texture features were calculated from histograms derived from T2 weighted imaging (T2WI), DWI, ADC, and T2 maps. Stepwise logistic regression analysis was used to screen independent predictors of LNM from clinical features, imaging features, and histogram features. Predictive performance was evaluated by receiver operating characteristic (ROC) curve analysis. Finally, a nomogram was established for predicting the risk of LNM.

RESULTS

The clinical, imaging and histogram features were analyzed by stepwise logistic regression. Preoperative carbohydrate antigen 199 level (p = 0.009), MRN stage (p < 0.001), _T2WIKurtosis (p = 0.010), _DWIMode (p = 0.038), _DWICV (p = 0.038), and _T2-mapP5 (p = 0.007) were independent predictors of LNM. These factors were combined to form the best predictive model. The model reached an area under the ROC curve (AUC) of 0.860, with a sensitivity of 72.8% and a specificity of 85.5%.

CONCLUSION

The histogram features on multi-parametric MRI of the primary tumor in rectal cancer were related to LN status, which is helpful for improving the ability to predict LNM of stage T3 rectal cancer.

Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer

Objective

This study aimed to develop a dual-energy spectral computed tomography (DESCT) nomogram that incorporated both clinical factors and DESCT parameters for individual preoperative prediction of lymph node metastasis (LNM) in patients with colorectal cancer (CRC).

Material and Methods

We retrospectively reviewed 167 pathologically confirmed patients with CRC who underwent enhanced DESCT preoperatively, and these patients were categorized into training (n = 117) and validation cohorts (n = 50). The monochromatic CT value, iodine concentration value (IC), and effective atomic number (Eff-Z) of the primary tumors were measured independently in the arterial phase (AP) and venous phase (VP) by two radiologists. DESCT parameters together with clinical factors were input into the prediction model for predicting LNM in patients with CRC. Logistic regression analyses were performed to screen for significant predictors of LNM, and these predictors were presented as an easy-to-use nomogram. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram.

Results

The logistic regression analysis showed that carcinoembryonic antigen, carbohydrate antigen 199, pericolorectal fat invasion, ICAP, ICVP, and Eff-ZVP were independent predictors in the predictive model. Based on these predictors, a quantitative nomogram was developed to predict individual LNM probability. The area under the curve (AUC) values of the nomogram were 0.876 in the training cohort and 0.852 in the validation cohort, respectively. DCA showed that our nomogram has outstanding clinical utility.

Conclusions

This study presents a clinical nomogram that incorporates clinical factors and DESCT parameters and can potentially be used as a clinical tool for individual preoperative prediction of LNM in patients with CRC.

Texture Analysis in the Assessment of Rectal Cancer: Comparison of T2WI and Diffusion-Weighted Imaging

Texture analysis (TA) techniques derived from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps of rectal cancer can both achieve good diagnosis performance. This study was to compare TA from T2WI and ADC maps between different pathological T and N stages to confirm which TA analysis is better in diagnosis performance. 146 patients were enrolled in this study. Tumor TA was performed on every patient's T2WI and ADC maps, respectively; then, skewness, kurtosis, uniformity, entropy, energy, inertia, and correlation were calculated. Our results demonstrated that those significant different parameters derived from T2WI had better diagnostic performance than those from ADC maps in differentiating pT3b-4 and pN1-2 stage tumors. In particular, the energy derived from T2WI was an optimal parameter for diagnostic efficiency. High-resolution T2WI plays a key point in the local stage of rectal cancer; thus, TA derived from T2WI may be a more useful tool to aid radiologists and surgeons in selecting treatment.

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.

Histogram models based on intravoxel incoherent motion diffusion-weighted imaging to predict nodal staging of rectal cancer

PURPOSE

To develop a model based on histogram parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the nodal staging of rectal cancer (RC).

MATERIAL AND METHODS

A total of 95 RC patients who underwent direct surgical resection were enrolled in this prospective study. The nodal staging on conventional magnetic resonance imaging (MRI) was evaluated according to the short axis diameter and morphological characteristics. Histogram parameters were extracted from apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) maps. Multivariate binary logistic regression analysis was conducted to establish models for predicting nodal staging among all patients and those underestimated on conventional MRI.

RESULTS

The combined model based on multiple maps demonstrated superior diagnostic performance to single map models, with an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.959, 94.3%, 88.3%, and 90.5%, respectively. The AUC of the combined model was significantly higher than that of the conventional nodal staging (P < 0.001). Additionally, 85.0% of the underestimated patients had suspicious lymph nodes with 5-8 mm short-axis diameter. The histogram model for these subgroups of patients showed good diagnostic efficacy with an AUC, sensitivity, specificity, and accuracy of 0.890, 100%, 75%, and 80.5%.

CONCLUSION

The histogram model based on IVIM-DWI could improve the diagnostic performance of nodal staging of RC. In addition, histogram parameters of IVIM-DWI may help to reduce the uncertainty of nodal staging in underestimated patients on conventional MRI.

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

Purpose

To evaluate the potential of the texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intratumoral subregions to distinguish benign from malignant breast tumors.

Materials and Methods

A total of 299 patients with pathologically verified breast tumors who underwent breast DCE-MRI examination were enrolled in this study, including 124 benign cases and 175 malignant cases. The whole tumor area was semi-automatically segmented on the basis of subtraction images of DCE-MRI in Matlab 2018b. According to the time to peak of the contrast agent, the whole tumor area was partitioned into three subregions: early, moderate, and late. A total of 467 texture features were extracted from the whole tumor area and the three subregions, respectively. Patients were divided into training (n = 209) and validation (n = 90) cohorts by different MRI scanners. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal feature subset in the training cohort. The Kolmogorov-Smirnov test was first performed on texture features selected by LASSO to test whether the samples followed a normal distribution. Two machine learning methods, decision tree (DT) and support vector machine (SVM), were used to establish classification models with a 10-fold cross-validation method. The performance of the classification models was evaluated with receiver operating characteristic (ROC) curves.

Results

In the training cohort, the areas under the ROC curve (AUCs) for the DT_Whole model and SVM_Whole model were 0.744 and 0.806, respectively. In contrast, the AUCs of the DT_Early model (P = 0.004), DT_Late model (P = 0.015), SVM_Early model (P = 0.002), and SVM_Late model (P = 0.002) were significantly higher: 0.863 (95% CI, 0.808-0.906), 0.860 (95% CI, 0.806-0.904), 0.934 (95% CI, 0.891-0.963), and 0.921 (95% CI, 0.876-0.954), respectively. The SVM_Early model and SVM_Late model achieved better performance than the DT_Early model and DT_Late model (P = 0.003, 0.034, 0.008, and 0.026, respectively). In the validation cohort, the AUCs for the DT_Whole model and SVM_Whole model were 0.670 and 0.708, respectively. In comparison, the AUCs of the DT_Early model (P = 0.006), DT_Late model (P = 0.043), SVM_Early model (P = 0.001), and SVM_Late model (P = 0.007) were significantly higher: 0.839 (95% CI, 0.747-0.908), 0.784 (95% CI, 0.601-0.798), 0.890 (95% CI, 0.806-0.946), and 0.865 (95% CI, 0.777-0.928), respectively.

Conclusion

The texture features from intratumoral subregions of breast DCE-MRI showed potential in identifying benign and malignant breast tumors.

Combining quantitative and qualitative magnetic resonance imaging features to differentiate anorectal malignant melanoma from low rectal cancer

Background

Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings. We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging (MRI) features could differentiate anorectal malignant melanoma from low rectal cancer.

Methods

Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent pre-operative rectal MRI from three hospitals were retrospectively enrolled. All patients were divided into the primary cohort (N = 84) and validation cohort (N = 51). Quantitative image analysis was performed on T1-weighted (T1WI), T2-weighted (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI). The subjective qualitative MRI findings were evaluated by two radiologists in consensus. Multivariable analysis was performed using stepwise logistic regression. The discrimination performance was assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI).

Results

The skewness derived from T2WI (T2WI-skewness) showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer (primary cohort: AUC = 0.852, 95% CI 0.788-0.916; validation cohort: 0.730, 0.645-0.815). Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors, and incorporating both factors achieved good discrimination performance in two cohorts (primary cohort: AUC = 0.913, 95% CI 0.868-0.958; validation cohort: 0.902, 0.844-0.960).

Conclusions

Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer. The quantitative image analysis helps improve diagnostic accuracy.

Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI

Simple Summary

Part I is an overview aimed to investigate some technical principles and the main fields of radiomic application in gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy in gastrointestinal cancers, describing mostly the results for each pre-eminent tumor. In particular, this paper provides a general description of the main radiomic drawbacks and future challenges, which limit radiomic application in clinical setting as routine. Further investigations need to standardize and validate the Radiomics as a helpful tool in management of oncologic patients. In that context, Radiomics has been playing a relevant role and could be considered as a future imaging landscape.

Abstract

Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.

Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients

Purpose

To develop and validate a radiomics nomogram based on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) features for the preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.

Materials and Methods

One hundred and sixty-two patients with rectal cancer confirmed by pathology were retrospectively analyzed, who underwent T2WI and DWI sequences. The data sets were divided into training (n = 97) and validation (n = 65) cohorts. For each case, a total of 2,752 radiomic features were extracted from T2WI, and ADC images derived from diffusion-weighted imaging. A two-sample t-test was used for prefiltering. The least absolute shrinkage selection operator method was used for feature selection. Three radiomics scores (rad-scores) (rad-score 1 for T2WI, rad-score 2 for ADC, and rad-score 3 for the combination of both) were calculated using the support vector machine classifier. Multivariable logistic regression analysis was then used to construct a radiomics nomogram combining rad-score 3 and independent risk factors. The performances of three rad-scores and the nomogram were evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to assess the clinical usefulness of the radiomics nomogram.

Results

The AUCs of the rad-score 1 and rad-score 2 were 0.805, 0.749 and 0.828, 0.770 in the training and validation cohorts, respectively. The rad-score 3 achieved an AUC of 0.879 in the training cohort and an AUC of 0.822 in the validation cohort. The radiomics nomogram, incorporating the rad-score 3, age, and LN size, showed good discrimination with the AUC of 0.937 for the training cohort and 0.884 for the validation cohort. DCA confirmed that the radiomics nomogram had clinical utility.

Conclusions

The radiomics nomogram, incorporating rad-score based on features from the T2WI and ADC images, and clinical factors, has favorable predictive performance for preoperative prediction of LN metastasis in patients with rectal cancer.

Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer

Background

An accurate assessment of lymph node (LN) status in patients with rectal cancer is important for treatment planning and an essential factor for predicting local recurrence and overall survival. In this study, we explored the potential value of histogram parameters of synthetic magnetic resonance imaging (SyMRI) in predicting LN metastasis in rectal cancer and compared their predictive performance with traditional morphological characteristics and chemical shift effect (CSE).

Methods

A total of 70 patients with pathologically proven rectal adenocarcinoma who received direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI, including SyMRI, were performed, and morphological characteristics and CSE of LN were assessed. Histogram parameters were extracted on a T1 map, T2 map, and proton density (PD) map, including mean, variance, maximum, minimum, 10th percentile, median, 90th percentile, energy, kurtosis, entropy, and skewness. Receiver operating characteristic (ROC) curves were used to explore their predictive performance for assessing LN status.

Results

Significant differences in the energy of the T1, T2, and PD maps were observed between LN-negative and LN-positive groups [all P<0.001; the area under the ROC curve (AUC) was 0.838, 0.858, and 0.823, respectively]. The maximum and kurtosis of the T2 map, maximum, and variance of PD map could also predict LN metastasis with moderate diagnostic power (P=0.032, 0.045, 0.016, and 0.047, respectively). Energy of the T1 map [odds ratio (OR) =1.683, 95% confidence interval (CI): 1.207-2.346, P=0.002] and extramural venous invasion on MRI (mrEMVI) (OR =10.853, 95% CI: 2.339-50.364, P=0.002) were significant predictors of LN metastasis. Moreover, the T1 map energy significantly improved the predictive performance compared to morphological features and CSE (P=0.0002 and 0.0485).

Conclusions

The histogram parameters derived from SyMRI of the primary tumor were associated with LN metastasis in rectal cancer and could significantly improve the predictive performance compared with morphological features and CSE.

The Heterogeneity of Skewness in T2W-Based Radiomics Predicts the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing primary staging with MRI were retrospectively evaluated, and 40 patients were finally selected. The ROIs were manually outlined in the tumour site on T2w sequences in the oblique-axial plane. Based on the TRG, patients were grouped as having either a complete or a partial response (TRG = (0,1), n = 15). NR patients had a minimal or poor nCRT response (TRG = (2,3), n = 25). Eighty-four local first-order radiomic features (RFs) were extracted from tumour ROIs. Only single RFs were investigated. Each feature was selected using univariate analysis guided by a one-tailed Wilcoxon rank-sum. ROC curve analysis was performed, using AUC computation and the Youden index (YI) for sensitivity and specificity. The RF measuring the heterogeneity of local skewness of T2w values from tumour ROIs differentiated Rs and NRs with a p-value ≈ 10⁻⁵; AUC = 0.90 (95%CI, 0.73–0.96); and YI = 0.68, corresponding to 80% sensitivity and 88% specificity. In conclusion, higher heterogeneity in skewness maps of the baseline tumour correlated with a greater benefit from nCRT.

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.

Histogram Analysis of Diffusion-Weighted Magnetic Resonance Imaging as a Biomarker to Predict Lymph Node Metastasis in T3 Stage Rectal Carcinoma

Purpose

This study investigated the predictive value of apparent diffusion coefficient (ADC) histogram parameters of the primary tumor for regional lymph node metastasis (LNM) in pathological T3 stage rectal cancer.

Patients and Methods

We retrospectively studied 175 patients with T3 stage rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging, between January 2015 and October 2017. Based on pathological analysis of surgical specimens, 113 patients were classified into the LN− group and 62 in the LN+ group. We analyzed clinical data, radiological characteristics and histogram parameters derived from ADC maps. Then, receiver operating characteristic curve (ROC) analyses were generated to determine the best diagnostic performance.

Results

The mean (p=0.002, cutoff=1.08×10^–3 s/mm²), coefficient of variation (CV) (p=0.040, cutoff=0.249) of the ADC map, carbohydrate antigen 199, and N stage with magnetic resonance (mrN stage) were independent factors for LNM. Combining these factors yielded the best diagnostic performance, with the area under the ROC curve of 0.838, 72.9% sensitivity, 79.1% specificity, 65.2% positive predictive value, and 84.5% negative predictive value.

Conclusion

With the mean >1.08×10^–3 s/mm² and CV <0.249, the ADC improved the diagnostic performance of LNM in T3 stage rectal cancer, which could assist surgeons with neoadjuvant chemoradiotherapy.

Prediction of Platinum-based Chemotherapy Response in Advanced High-grade Serous Ovarian Cancer: ADC Histogram Analysis of Primary Tumors

RATIONALE AND OBJECTIVES

To investigate the feasibility of apparent diffusion coefficient (ADC) histogram analysis of primary advanced high-grade serous ovarian cancer (HGSOC) to predict patient response to platinum-based chemotherapy.

MATERIALS AND METHODS

A total of 70 patients with 102 advanced stage HGSOCs (International Federation of Gynecology and Obstetrics (FIGO) stages III-IV) who received standard treatment of primary debulking surgery followed by the first line of platinum-based chemotherapy were retrospectively enrolled. Patients were grouped as platinum-resistant and platinum-sensitive according to whether relapse occurred within 6 months. Clinical characteristics, including age, pretherapy CA125 level, International Federation of Gynecology and Obstetrics stage, residual tumor, and histogram parameters derived from whole tumor and solid component such as ADC_mean; 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th percentiles; skewness and kurtosis, were compared between platinum-resistant and platinum-sensitive groups.

RESULTS

No significantly different clinical characteristics were observed between platinum-sensitive and platinum-resistant patients. There were no significant differences in any whole-tumor histogram-derived parameters between the two groups. Significantly higher ADC_mean and percentiles and significantly lower skewness and kurtosis from the solid-component histogram parameters were observed in the platinum-sensitive group when compared with the platinum-resistant group. ADC_mean, skewness and kurtosis showed moderate prediction performances, with areas under the curve of 0.667, 0.733 and 0.616, respectively. Skewness was an independent risk factor for platinum resistance.

CONCLUSION

Pretreatment ADC histogram analysis of primary tumors has the potential to allow prediction of response to platinum-based chemotherapy in patients with advanced HGSOC.

Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer

Objective

To develop and validate a multiregional-based magnetic resonance imaging (MRI) radiomics model and combine it with clinical data for individual preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.

Methods

186 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 123) and testing cohorts (n = 63). Spearman’s rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Five support vector machine (SVM) classification models were built using selected clinical and semantic variables, single-regional radiomics features, multiregional radiomics features, and combinations, for predicting LN metastasis in rectal cancer. The performance of the five SVM models was evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the testing cohort. Differences in the AUCs among the five models were compared using DeLong’s test.

Results

The clinical, single-regional radiomics and multiregional radiomics models showed moderate predictive performance and diagnostic accuracy in predicting LN metastasis with an AUC of 0.725, 0.702, and 0.736, respectively. A model with improved performance was created by combining clinical data with single-regional radiomics features (AUC = 0.827, (95% CI, 0.711–0.911), P = 0.016). Incorporating clinical data with multiregional radiomics features also improved the performance (AUC = 0.832 (95% CI, 0.717–0.915), P = 0.015).

Conclusion

Multiregional-based MRI radiomics combined with clinical data can improve efficacy in predicting LN metastasis and could be a useful tool to guide surgical decision-making in patients with rectal cancer.

Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions

Purpose

To compare the performance of histogram analysis and intra-perinodular textural transition (Ipris) for distinguishing between benign and malignant testicular lesions.

Patients and Methods

This retrospective study included 76 patients with 80 pathologically confirmed testicular lesions (55 malignant, 25 benign). All patients underwent preoperative T2-weighted imaging (T2WI) on a 3.0T MR scanner. All testicular lesions were manually segmented on axial T2WI, and histogram and Ipris features were extracted. Thirty enrolled patients were randomly selected to estimate the robustness of the features. We used intraclass correlation coefficients (ICCs) to evaluate intra- and interobserver agreement of features, independent t-test or Mann–Whitney U-test to compare features between benign and malignant lesions, and receiver operating characteristic curve analysis to evaluate the diagnostic performance of features.

Results

Eighteen histogram features and forty-eight Ipris features were extracted from T2WI of each lesion. Most (60/66) histogram and Ipris features had good robustness (ICC of both intra- and interobserver variabilities >0.6). Three histogram and nine Ipris features were significantly different between the benign and malignant groups. The area under the curve values for Energy, TotalEnergy, and Ipris_shell1_id_std were 0.807, 0.808, and 0.708, respectively, which were relatively higher than those of other features.

Conclusion

Ipris features may be useful for identifying benign and malignant testicular tumors but have no significant advantage over conventional histogram features.

MRI-Based Radiomic Model for Preoperative Risk stratification in Stage I Endometrial Cancer

Introduction: Preoperative risk stratification is crucial for clinical treatment of endometrial cancer (EC). This study aimed to establish a model based on magnetic resonance imaging (MRI) and clinical factors for risk classification of EC.

Materials and Methods: A total of 102 patients with pathologically proven Stage I EC were included. Preoperative MRI examinations were performed in all the patients. 720 radiomic features were extracted from T2-weighted images. Least absolute shrinkage and selection operator (LASSO) regression model was performed to reduce irrelevant features. Logistic regression was used to build clinical, radiomic and combined predictive models. A nomogram was developed for clinical application.

Results: The radiomic model has a better performance than the model based on clinical and conventional MRI characteristics [AUC of 0.946 (95% CI: 0.882-0.973) vs AUC of 0.756 (95% CI: 0.65, 0.86)]. The combined model consisting of radiomic features and tumor size showed the best predictive performance in the training cohort with AUC of 0.955 in the training and 0.889 in the validation cohorts.

Conclusions: MRI-based radiomic model has great potential in prediction of low-risk ECs.

Texture Analysis Based on Sagittal Fat-Suppression and Transverse T2-Weighted Magnetic Resonance Imaging for Determining Local Invasion of Rectal Cancer

Background: Accurate evaluation of local invasion (T-stage) of rectal cancer is essential for treatment planning. A search of PubMed database indicated that the correlation between texture features from T2-weighted magnetic resonance imaging (T2WI) (MRI) and T-stage has not been explored extensively.

Purpose: To evaluate the performance of texture analysis using sagittal fat-suppression combined with transverse T2WI for determining T-stage of rectal cancer.

Methods: One hundred and seventy-four rectal cancer cases who underwent preoperative MRI were retrospectively selected and divided into high (T3/4) and low (T1/2) T-stage groups. Texture features were, respectively, extracted from sagittal fat-suppression and transverse T2WI images. Univariate and multivariate analyses were conducted to determine T-stage. Discrimination performance was assessed by receiver operating characteristic (ROC) analysis.

Results: For univariate analysis, the best performance in differentiating T1/2 from T3/4 tumors was achieved from transverse T2WI, and the area under the ROC curve (AUC) was 0.740. For multivariate analysis, the logical regression model incorporating the independent predictors achieved an AUC of 0.789.

Conclusions: Texture features from sagittal fat-suppression combined with transverse T2WI presented moderate association with T-stage of rectal cancer. These findings may be valuable in selecting optimum treatment strategy.

Application of Texture Analysis Based on Sagittal Fat-Suppression and Oblique Axial T2-Weighted Magnetic Resonance Imaging to Identify Lymph Node Invasion Status of Rectal Cancer

Objective: To investigate the value of texture features derived from T2-weighted magnetic resonance imaging (T2WI) for predicting preoperative lymph node invasion (N stage) in rectal cancer.

Materials and Methods: One hundred and eighty-two patients with histopathologically confirmed rectal cancer and preoperative magnetic resonance imaging were retrospectively analyzed, who were divided into high (N1-2) and low N stage (N0). Texture features were calculated from histogram, gray-level co-occurrence matrix, and gray-level run-length matrix from sagittal fat-suppression and oblique axial T2WI. Independent sample t-test or Mann-Whitney U-test were used for statistical analysis. Multivariate logistic regression analysis was conducted to build the predictive models. Predictive performance was evaluated by receiver operating characteristic (ROC) analysis.

Results: Energy (ENE), entropy (ENT), information correlation (INC), long-run emphasis (LRE), and short-run low gray-level emphasis (SRLGLE) extracted from sagittal fat-suppression T2WI, and ENE, ENT, INC, low gray-level run emphasis (LGLRE), and SRLGLE from oblique axial T2WI were significantly different between stage N0 and stage N1-2 tumors. The multivariate analysis for features from sagittal fat-suppression T2WI showed that higher SRLGLE and lower ENE were independent predictors of lymph node invasion. The model reached an area under ROC curve (AUC) of 0.759. The analysis for features from oblique axial T2WI showed that higher INC and SRLGLE were independent predictors. The model achieved an AUC of 0.747. The analysis for all extracted features showed that lower ENE from sagittal fat-suppression T2WI and higher INC and SRLGLE from oblique axial T2WI were independent predictors. The model showed an AUC of 0.772.

Conclusions: Texture features derived from T2WI could provide valuable information for identifying the status of lymph node invasion in rectal cancer.

Texture Analysis of Breast DCE-MRI Based on Intratumoral Subregions for Predicting HER2 2+ Status

Background: Breast tumor heterogeneity is related to risk factors that lead to aggressive tumor growth; however, such heterogeneity has not been thoroughly investigated.

Purpose: To evaluate the performance of texture features extracted from heterogeneity subregions on subtraction MRI images for identifying human epidermal growth factor receptor 2 (HER2) 2+ status of breast cancers.

Materials and Methods: Seventy-six patients with HER2 2+ breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging were enrolled, including 42 HER2 positive and 34 negative cases confirmed by fluorescence in situ hybridization. The lesion area was delineated semi-automatically on the subtraction MRI images at the second, fourth, and sixth phases (P-1, P-2, and P-3). A regionalization method was used to segment the lesion area into three subregions (rapid, medium, and slow) according to peak arrival time of the contrast agent. We extracted 488 texture features from the whole lesion area and three subregions independently. Wrapper, least absolute shrinkage and selection operator (LASSO), and stepwise methods were used to identify the optimal feature subsets. Univariate analysis was performed as well as support vector machine (SVM) with a leave-one-out-based cross-validation method. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the classifiers.

Results: In univariate analysis, the variance from medium subregion at P-2 was the best-performing feature for distinguishing HER2 2+ status (AUC = 0.836); for the whole lesion region, the variance at P-2 achieved the best performance (AUC = 0.798). There was no significant difference between the two methods (P = 0.271). In the machine learning with SVM, the best performance (AUC = 0.929) was achieved with LASSO from rapid subregion at P-2; for the whole region, the highest AUC value was 0.847 obtained at P-2 with LASSO. The difference was significant between the two methods (P = 0.021).

Conclusion: The texture analysis of heterogeneity subregions based on intratumoral regionalization method showed potential value for recognizing HER2 2+ status in breast cancer.

Whole-Liver Apparent Diffusion Coefficient Histogram Analysis for the Diagnosis and Staging of Liver Fibrosis

BACKGROUND

Conventional diffusion-weighted imaging is limited in the quantitative evaluation of liver fibrosis, and whole-liver apparent diffusion coefficient (ADC) histogram analysis might contribute to the diagnosis and staging of liver fibrosis.

PURPOSE

To explore the value of whole-liver ADC histogram parameters in the diagnosis and staging of liver fibrosis.

STUDY TYPE

Retrospective.

POPULATION

Twenty individuals with no liver disease and 86 patients with liver fibrosis, including 30 with chronic viral hepatitis, 29 with autoimmune hepatitis, and 27 with unexplained liver fibrosis patients.

FIELD STRENGTH/SEQUENCE

3.0T/T₁ -weighted, T₂ -weighted, and diffusion-weighted images.

ASSESSMENT

A region of interest (ROI) was drawn in each slice of the diffusion-weighted images. Whole-liver histogram parameters were obtained with dedicated software by accumulating all ROIs. The effectiveness of the parameters in differentiating stage 1 or greater (≥F1), stage 2 or greater (≥F2), and stage 3 or greater (≥F3) liver fibrosis was assessed.

STATISTICAL TESTS

Mann-Whitney U-test and receiver operating characteristic curve analysis.

RESULTS

Kurtosis, entropy, skewness, mode, and 90^th and 75^th percentiles exhibited significant differences among the pathological fibrosis stages (P < 0.05). Kurtosis was found to be the most meaningful parameter in differentiating fibrosis stages of the viral hepatitis, autoimmune hepatitis, and unexplained liver fibrosis group (area under the curve) (AUC = 0.793, 0.771, 0.798, respectively). In the combined liver fibrosis group, kurtosis achieved the highest AUC (0.801; 95% confidence interval [CI]: 0.702-0.900; sensitivity: 0.750; specificity: 0.850; positive likelihood ratio: 4.953; negative likelihood ratio: 0.302; positive predictive value: 0.946; negative predictive value: 0.486), with a cutoff value of 1.817, in differentiating fibrosis stage ≥F1.

DATA CONCLUSION

Kurtosis, entropy, skewness, mode, and 90^th and 75^th percentiles may contribute to the diagnosis and staging of liver fibrosis, especially kurtosis.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1745-1754.

Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer

PURPOSE

To establish a radiomics nomogram integrating clinical factors and radiomics features from ultrasound for the preoperative diagnosis axillary lymph node (ALN) status in patients with early-stage invasive breast cancer (EIBC).

MATERIALS AND METHODS

Between September 2016 and December 2018, four hundred twenty-six ultrasound manually segmented images of patients with EIBC were enrolled in our retrospective study, which were divided into a primary cohort (n = 300) and a validation cohort (n = 126). A radiomics signature was built with the least absolute shrinkage and selection operator (LASSO) algorithm in the primary cohort. Multivariable logistic regression analysis was used to establish a radiomics nomogram model based on radiomics signature and clinical variables. The performance of nomogram was quantified with respect to discrimination and calibration. The radiomics model was further evaluated in the internal validation cohort.

RESULTS

The radiomics signature, consisted of fourteen selected ALN-status-related features, achieved moderate prediction efficacy with an area under the curve (AUC) of 0.78 and 0.71 in the primary and validation cohorts respectively. The radiomics nomogram, comprising tumor size, US-reported LN status and radiomics signature, showed good calibration and favorite performance for ALN detection (AUC 0.84 and 0.81 in the primary and validation cohort). The decision curve which was demonstrated the radiomics nomogram displayed good clinical utility.

CONCLUSION

The radiomics nomogram could hold promise as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to develop more effective preoperative decision-making.