Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer

Tumor habitat-based MRI features assessing early response in locally advanced nasopharyngeal carcinoma

OBJECTIVE

The early response to concurrent chemoradiotherapy in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) is closely correlated with prognosis. In this study, we aimed to predict early response using a combined model that combines sub-regional radiomics features from multi-sequence MRI with clinically relevant factors.

METHODS

A total of 104 patients with LA-NPC were randomly divided into training and test cohorts at a ratio of 3:1. Radiomic features were extracted from subregions within the tumor area using the K-means clustering method, and feature selection was performed using LASSO regression. Four models were established: a radiomics model, a clinical model, an Intratumor Heterogeneity (ITH) score-based model and a combined model that integrates the ITH score with clinical factors. The predictive performance of these models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

Among the models, the combined model incorporating the ITH score and clinical factors exhibited the highest predictive performance in the test cohort (AUC=0.838). Additionally, the models based on ITH score showed superior prognostic value in both the training cohort (AUC=0.888) and the test cohort (AUC=0.833).

CONCLUSION

The combined model that integrates the ITH score with clinical factors exhibited superior performance in predicting early response following concurrent chemoradiotherapy in patients with LA-NPC.

Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features

PURPOSE

We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients.

MATERIALS AND METHODS

The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared.

RESULTS

This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences.

CONCLUSIONS

Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.

Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study

OBJECTIVES

Exploring the value of adding correlation analysis (radiomic features (RFs) of pelvic metastatic lymph nodes and primary lesions) to screen RFs of primary lesions in the feature selection process of establishing prediction model.

METHODS

A total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM.

RESULTS

The AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively.

CONCLUSION

The lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.

Development and Validation of a Biparametric MRI Deep Learning Radiomics Model with Clinical Characteristics for Predicting Perineural Invasion in Patients with Prostate Cancer

RATIONALE AND OBJECTIVES

Perineural invasion (PNI) is an important prognostic biomarker for prostate cancer (PCa). This study aimed to develop and validate a predictive model integrating biparametric MRI-based deep learning radiomics and clinical characteristics for the non-invasive prediction of PNI in patients with PCa.

MATERIALS AND METHODS

In this prospective study, 557 PCa patients who underwent preoperative MRI and radical prostatectomy were recruited and randomly divided into the training and the validation cohorts at a ratio of 7:3. Clinical model for predicting PNI was constructed by univariate and multivariate regression analyses on various clinical indicators, followed by logistic regression. Radiomics and deep learning methods were used to develop different MRI-based radiomics and deep learning models. Subsequently, the clinical, radiomics, and deep learning signatures were combined to develop the integrated deep learning-radiomics-clinical model (DLRC). The performance of the models was assessed by plotting the receiver operating characteristic (ROC) curves and precision-recall (PR) curves, as well as calculating the area under the ROC and PR curves (ROC-AUC and PR-AUC). The calibration curve and decision curve were used to evaluate the model's goodness of fit and clinical benefit.

RESULTS

The DLRC model demonstrated the highest performance in both the training and the validation cohorts, with ROC-AUCs of 0.914 and 0.848, respectively, and PR-AUCs of 0.948 and 0.926, respectively. The DLRC model showed good calibration and clinical benefit in both cohorts.

CONCLUSION

The DLRC model, which integrated clinical, radiomics, and deep learning signatures, can serve as a robust tool for predicting PNI in patients with PCa, thus aiding in developing effective treatment strategies.

Magnetic resonance imaging-based radiomics nomogram for the evaluation of therapeutic responses to neoadjuvant chemohormonal therapy in high-risk non-metastatic prostate cancer

PURPOSE

The aim of this study was to assess the potential application of a radiomics features-based nomogram for predicting therapeutic responses to neoadjuvant chemohormonal therapy (NCHT) in patients with high-risk non-metastatic prostate cancer (PCa).

METHODS

Clinicopathologic information was retrospectively collected from 162 patients with high-risk non-metastatic PCa receiving NCHT and radical prostatectomy at our center. The postoperative pathological findings were used as the gold standard for evaluating the efficacy of NCHT. The least absolute shrinkage and selection operator (LASSO) was conducted to develop radiomics signature. Multivariate logistic regression analyses were conducted to identify the predictors of a positive pathological response to NCHT, and a nomogram was constructed based on these predictors.

RESULTS

Sixty-three patients (38.89%) experienced positive pathological response to NCHT. Receiver operating characteristic analyses showed that the area under the curve (AUC) of periprostatic fat (PPF) radiomics signature was 0.835 (95% CI, 0.754-0.898), while the AUC of intratumoral radiomics signature was 0.822 (95% CI, 0.739-0.888). Multivariate logistic regression analysis revealed that PSA level, PPF radiomics signature and intratumoral radiomics signature were independent predictors of positive pathological response. A nomogram based on these three predictors was constructed. The AUC was 0.908 (95% CI, 0.839-0.954). The Hosmer-Lemeshow goodness-of-fit test showed that the nomogram was well calibrated. Decision curve analysis revealed the favorable clinical practicability of the nomogram. The nomogram was successfully validated in the validation cohort. Kaplan-Meier analyses showed that nomogram and positive pathological response were significantly related with survival of PCa.

CONCLUSION

The radiomics-clinical nomogram based on mpMRI radiomics features exhibited superior predictive ability for positive pathological response to NCHT in high-risk non-metastatic PCa.

AtPCa-Net: anatomical-aware prostate cancer detection network on multi-parametric MRI

Multi-parametric MRI (mpMRI) is widely used for prostate cancer (PCa) diagnosis. Deep learning models show good performance in detecting PCa on mpMRI, but domain-specific PCa-related anatomical information is sometimes overlooked and not fully explored even by state-of-the-art deep learning models, causing potential suboptimal performances in PCa detection. Symmetric-related anatomical information is commonly used when distinguishing PCa lesions from other visually similar but benign prostate tissue. In addition, different combinations of mpMRI findings are used for evaluating the aggressiveness of PCa for abnormal findings allocated in different prostate zones. In this study, we investigate these domain-specific anatomical properties in PCa diagnosis and how we can adopt them into the deep learning framework to improve the model's detection performance. We propose an anatomical-aware PCa detection Network (AtPCa-Net) for PCa detection on mpMRI. Experiments show that the AtPCa-Net can better utilize the anatomical-related information, and the proposed anatomical-aware designs help improve the overall model performance on both PCa detection and patient-level classification.

The practical clinical role of machine learning models with different algorithms in predicting prostate cancer local recurrence after radical prostatectomy

BACKGROUND

The detection of local recurrence for prostate cancer (PCa) patients following radical prostatectomy (RP) is challenging and can influence the treatment plan. Our aim was to construct and verify machine learning models with three different algorithms based on post-operative mpMRI for predicting local recurrence of PCa after RP and explore their potential clinical value compared with the Prostate Imaging for Recurrence Reporting (PI-RR) score of expert-level radiologists.

METHODS

A total of 176 patients were retrospectively enrolled and randomly divided into training (n = 123) and testing (n = 53) sets. The PI-RR assessments were performed by two expert-level radiologists with access to the operative histopathological and pre-surgical clinical results. The radiomics models to predict local recurrence were built by utilizing three different algorithms (i.e., support vector machine [SVM], linear discriminant analysis [LDA], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]). The combined model integrating radiomics features and PI-RR score was developed using the most effective classifier. The classification performances of the proposed models were assessed by receiver operating characteristic (ROC) curve analysis.

RESULTS

There were no significant differences between the training and testing sets concerning age, prostate-specific antigen (PSA), Gleason score, T-stage, seminal vesicle invasion (SVI), perineural invasion (PNI), and positive surgical margins (PSM). The radiomics model based on LR-LASSO exhibited superior performance than other radiomics models, with an AUC of 0.858 in the testing set; the PI-RR yielded an AUC of 0.833, and there was no significant difference between the best radiomics model and the PI-RR score. The combined model achieved the best predictive performance with an AUC of 0.924, and a significant difference was observed between the combined model and PI-RR score.

CONCLUSIONS

Our radiomics model is an effective tool to predict PCa local recurrence after RP. By integrating radiomics features with the PI-RR score, our combined model exhibited significantly better predictive performance of local recurrence than expert-level radiologists' PI-RR assessment.

Comparisons of mpMRI, 68Ga-PSMA PET/CT and mpMRI combined with 68Ga-PSMA PET/CT in diagnosing prostate cancer based on tumor detection, localization and staging

PURPOSE

To evaluate the diagnostic ability of mpMRI, 68Ga-PSMA PET/CT and mpMRI combined with 68Ga-PSMA PET/CT in detecting and localizing lesions, and further clarify the accuracy of these examinations in tumor staging.

METHODS

Seventy patients who underwent mpMRI, 68Ga-PSMA PET/CT and radical prostatectomy were enrolled. The abilities to detect index and clinically significant lesions by three examinations were compared. We further evaluated the ability of these examinations to localize lesions to the superior, inferior, anterior, posterior, left and right halves of the prostate and analyzed their accuracy in local and lymph node staging.

RESULTS

There were no significant differences among mpMRI, 68Ga-PSMA PET/CT and mpMRI combined with 68Ga-PSMA PET/CT in their ability to detect index (p = 0.48, p = 0.23 and p = 0.07) and clinically significant lesions (p = 0.30, p = 0.29 and p = 0.06) or to localize lesions in six half divisions of the prostate. With postoperative pathology as reference, both mpMRI (p = 0.10) and mpMRI combined with 68Ga-PSMA PET/CT (p = 0.10) can accurately assess the local staging of prostate cancer. However, 68Ga-PSMA PET/CT underestimates the local staging of prostate cancer (p < 0.01). Regarding lymph node staging, the three types of examination showed no significant differences compared to postoperative pathology (p = 0.63, p = 0.51 and p = 0.14).

CONCLUSIONS

With postoperative pathology as reference, 68Ga-PSMA PET/CT underestimates the local tumor staging. MpMRI combined with 68Ga-PSMA PET/CT has no obvious advantages in detecting, localizing or staging prostate cancer compared with mpMRI.

Can Machine Learning Models Detect and Predict Lymph Node Involvement in Prostate Cancer? A Comprehensive Systematic Review

(1) Background: Recently, Artificial Intelligence (AI)-based models have been investigated for lymph node involvement (LNI) detection and prediction in Prostate cancer (PCa) patients, in order to reduce surgical risks and improve patient outcomes. This review aims to gather and analyze the few studies available in the literature to examine their initial findings. (2) Methods: Two reviewers conducted independently a search of MEDLINE databases, identifying articles exploring AI's role in PCa LNI. Sixteen studies were selected, and their methodological quality was appraised using the Radiomics Quality Score. (3) Results: AI models in Magnetic Resonance Imaging (MRI)-based studies exhibited comparable LNI prediction accuracy to standard nomograms. Computed Tomography (CT)-based and Positron Emission Tomography (PET)-CT models demonstrated high diagnostic and prognostic results. (4) Conclusions: AI models showed promising results in LN metastasis prediction and detection in PCa patients. Limitations of the reviewed studies encompass retrospective design, non-standardization, manual segmentation, and limited studies and participants. Further research is crucial to enhance AI tools' effectiveness in this area.

Research on texture images and radiomics in urology: a review of urological MR imaging applications

PURPOSE OF REVIEW

Tumor volume and heterogenicity are associated with diagnosis and prognosis of urological cancers, and assessed by conventional imaging. Quantitative imaging, Radiomics, using advanced mathematical analysis may contain information imperceptible to the human eye, and may identify imaging-based biomarkers, a new field of research for individualized medicine. This review summarizes the recent literature on radiomics in kidney and prostate cancers and the future perspectives.

RECENT FINDINGS

Radiomics studies have been developed and showed promising results in diagnosis, in characterization, prognosis, treatment planning and recurrence prediction in kidney tumors and prostate cancer, but its use in guiding clinical decision-making remains limited at present due to several limitations including lack of external validations in most studies, lack of prospective studies and technical standardization.

SUMMARY

Future challenges, besides developing prospective and validated studies, include automated segmentation using artificial intelligence deep learning networks and hybrid radiomics integrating clinical data, combining imaging modalities and genomic features. It is anticipated that these improvements may allow identify these noninvasive, imaging-based biomarkers, to enhance precise diagnosis, improve decision-making and guide tailored treatment.

The predictive value of machine learning and nomograms for lymph node metastasis of prostate cancer: a systematic review and meta-analysis

BACKGROUND

In clinical practice, there are currently a variety of nomograms for predicting lymph node metastasis (LNM) of prostate cancer. At the same time, some scholars have introduced machine learning (ML) into the prediction of LNM of prostate cancer. However, the predictive value of nomograms and ML remains controversial. Based on this situation, this systematic review and meta-analysis was performed to explore the predictive value of various nomograms currently recommended and newly-developed ML models for LNM in prostate cancer patients.

EVIDENCE ACQUISITION

Cochrane, PubMed, Embase, and Web of Science were searched up to November 1, 2022. The risk of bias in the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). The concordance index (C-index), sensitivity, and specificity were adopted to evaluate the predictive accuracy of the models.

RESULTS

Thirty-one studies (18,803 patients) were included. Seven kinds of nomograms currently recommended, dominated by Briganti nomogram or MSKCC nomogram, were covered in the included studies. For newly-developed ML models, the C-index for LNM prediction in the training set and validation set was 0.846 [95%CI (0.818, 0.873)] and 0.862 [95%CI (0.819-0.905)] respectively. Most ML models in the training set were based on Logistic Regression (LR), which had a sensitivity of 0.78 [95%CI (0.70, 0.85)] and a specificity of 0.85 [95%CI (0.77, 0.90)] in the training set, and a sensitivity of 0.81 [95%CI (0.67, 0.89)] and a specificity of 0.82 [95%CI (0.75, 0.88)] in the validation set. For the recommended nomograms, the C-index in the validation set was 0.745 [95%CI (0.701, 0.790)] for the Briganti nomogram and 0.714 [95%CI (0.662, 0.765)] for the MSKCC nomogram.

CONCLUSION

The predictive accuracy of ML is superior to existing clinically recommended nomograms, and appropriate updates can be conducted to existing nomograms according to special situations.

Value of a combined magnetic resonance imaging-based radiomics-clinical model for predicting extracapsular extension in prostate cancer: a preliminary study

Background

Extracapsular extension (ECE) of prostate cancer (PCa) is closely related to the treatment and prognosis of patients, and radiomics has been widely used in the study of PCa. This study aimed to evaluate the value of a combined model considering magnetic resonance imaging (MRI)-based radiomics and clinical parameters for predicting ECE in PCa.

Methods

A total of 392 PCa patients enrolled in this retrospective study were randomly divided into the training and validation sets at a ratio of 7:3. Radiologists assessed all lesions by Mehralivand grade. Radiomics features were extracted and selected to build a radiomics model, while clinical parameters were noted to construct the clinical model. The combined model was constructed by the integration of the radiomics model and clinical model. Meanwhile, the nomogram for predicting ECE was constructed based on the combined model. Then, the area under the receiver operating characteristic (ROC) curve (AUC), Delong test and the decision curve analysis (DCA) were used to compare the performance among the combined model, radiomics model, clinical model and Mehralivand grade.

Results

The AUC of the combined model in the validation set was comparable to that of the radiomics model [AUC =0.894 (95% confidence interval (CI): 0.837-0.950) vs. 0.835 (95% CI: 0.763-0.908), P>0.05]. In addition, the sensitivity of the combined model and radiomics model was 90.7% and 77.8%, with an accuracy of 81.4% and 76.3%, respectively. On the other hand, the AUCs of the Mehralivand grade of radiologists and clinical model were 0.774 (95% CI: 0.691-0.857) and 0.749 (95% CI: 0.658-0.840), respectively, in the validation set, which were lower than those in the combined model (P<0.05). The DCA implied that the combined model could obtain the maximum net clinical benefits compared with the clinical model, the Mehralivand grade and radiomics model.

Conclusions

The combined model has a satisfactory predictive value for ECE in PCa patients compared with the clinical model, Mehralivand grade of radiologists, and the radiomics model.

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.

The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature

The development of precise medical imaging has facilitated the establishment of radiomics, a computer-based method of quantitatively analyzing subvisual imaging characteristics. The present review summarizes the current literature on the use of diagnostic magnetic resonance imaging (MRI)-derived radiomics in prostate cancer (PCa) risk stratification. A stepwise literature search of publications from 2017 to 2022 was performed. Of 218 articles on MRI-derived prostate radiomics, 33 (15.1%) generated models for PCa risk stratification. Prediction of Gleason score (GS), adverse pathology, postsurgical recurrence, and postradiation failure were the primary endpoints in 15 (45.5%), 11 (33.3%), 4 (12.1%), and 3 (9.1%) studies. In predicting GS and adverse pathology, radiomic models differentiated well, with receiver operator characteristic area under the curve (ROC-AUC) values of 0.50–0.92 and 0.60–0.92, respectively. For studies predicting post-treatment recurrence or failure, ROC-AUC for radiomic models ranged from 0.73 to 0.99 in postsurgical and radiation cohorts. Finally, of the 33 studies, 7 (21.2%) included external validation. Overall, most investigations showed good to excellent prediction of GS and adverse pathology with MRI-derived radiomic features. Direct prediction of treatment outcomes, however, is an ongoing investigation. As these studies mature and reach potential for clinical integration, concerted effort to validate these radiomic models must be undertaken.

Beyond diagnosis: is there a role for radiomics in prostate cancer management?

The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.

Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage

OBJECTIVES

Post-stroke epilepsy (PSE) is associated with increased morbidity and mortality. This study aimed to develop and validate a novel prediction model combining clinical factors and radiomics features to accurately identify patients at high risk of developing PSE after intracerebral haemorrhage (ICH).

METHODS

Researchers performed a retrospective medical chart review to extract derivation and validation cohorts of patients with first-ever ICH that attended two tertiary hospitals in China between 2010 and 2020. Clinical data were extracted from electronic medical records and supplemented by tele-interview. Predictive clinical variables were selected by multivariable logistic regression to build the clinical model. Predictive radiomics features were identified, and a Rad-score was calculated according to the coefficient of the selected feature. Both clinical variables and radiomic features were combined to build the radiomics-clinical model. Performances of the clinical, Rad-score, and combined models were compared.

RESULTS

A total of 1571 patients were included in the analysis. Cortical involvement, early seizures within 7 days of ICH, NIHSS score, and ICH volume were included in the clinical model. Rad-score, instead of ICH volume, was included in the combined model. The combined model exhibited better discrimination ability and achieved an overall better benefit against threshold probability than the clinical model in the decision curve analysis (DCA).

CONCLUSIONS

The combined radiomics-clinical model was better able to predict ICH-associated PSE compared to the clinical model. This can help clinicians better predict an individual patient's risk of PSE following a first-ever ICH and facilitate earlier PSE diagnosis and treatment.

KEY POINTS

• Radiomics has not been used in predicting the risk of developing PSE. • Higher Rad-scores were associated with higher risk of developing PSE. • The combined model showed better performance of PSE prediction ability.

Novel, alternative splicing signature to detect lymph node metastasis in prostate adenocarcinoma with machine learning

Background

The presence of lymph node metastasis leads to a poor prognosis for prostate cancer (Pca). Recently, many studies have indicated that gene signatures may be able to predict the status of lymph nodes. The purpose of this study is to probe and validate a new tool to predict lymph node metastasis (LNM) based on alternative splicing (AS).

Methods

Gene expression profiles and clinical information of prostate adenocarcinoma cohort were retrieved from The Cancer Genome Atlas (TCGA) database, and the corresponding RNA-seq splicing events profiles were obtained from the TCGA SpliceSeq. Limma package was used to identify the differentially expressed alternative splicing (DEAS) events between LNM and non-LNM groups. Eight machine learning classifiers were built to train with stratified five-fold cross-validation. SHAP values was used to explain the model.

Results

333 differentially expressed alternative splicing (DEAS) events were identified. Using correlation filter and the least absolute shrinkage and selection operator (LASSO) method, a 96 AS signature was identified that had favorable discrimination in the training set and validated in the validation set. The linear discriminant analysis (LDA) was the best classifier after 100 iterations of training. The LDA classifier was able to distinguish between LNM and non-LNM with an area under the receiver operating curve of 0.962 ± 0.026 in the training set (D1 = 351) and 0.953 in the validation set (D2 = 62). The decision curve analysis plot proved the clinical application of the AS-based model.

Conclusion

Machine learning combined with AS data could robustly distinguish between LNM and non-LNM in Pca.

Radiomics Signature Using Manual Versus Automated Segmentation for Lymph Node Staging of Bladder Cancer

BACKGROUND

Bladder cancer (BC) treatment algorithms depend on accurate tumor staging. To date, computed tomography (CT) is recommended for assessment of lymph node (LN) metastatic spread in muscle-invasive and high-risk BC. However, the diagnostic efficacy of radiologist-evaluated CT imaging studies is limited.

OBJECTIVE

To evaluate the performance of quantitative radiomics signatures for detection of LN metastases in BC.

DESIGN, SETTING, AND PARTICIPANTS

Of 1354 patients with BC who underwent radical cystectomy (RC) with lymphadenectomy who were screened, 391 with pathological nodal staging (pN0: n = 297; pN+: n = 94) were included and randomized into training (n = 274) and test (n = 117) cohorts. Pelvic LNs were segmented manually and automatically. A total of 1004 radiomics features were extracted from each LN and a machine learning model was trained to assess pN status using histopathology labels as the ground truth.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Radiologist assessment was compared to radiomics-based analysis using manual and automated LN segmentations for detection of LN metastases in BC. Statistical analysis was performed using the receiver operating characteristics curve method and evaluated in terms of sensitivity, specificity, and area under the curve (AUC).

RESULTS AND LIMITATIONS

In total, 1845 LNs were manually segmented. Automated segmentation correctly located 361/557 LNs in the test cohort. Manual and automatic masks achieved an AUC of 0.80 (95% confidence interval [CI] 0.69-0.91; p = 0.64) and 0.70 (95% CI: 0.58-0.82; p = 0.17), respectively, in the test cohort compared to radiologist assessment, with an AUC of 0.78 (95% CI 0.67-0.89). A combined model of a manually segmented radiomics signature and radiologist assessment reached an AUC of 0.81 (95% CI 0.71-0.92; p = 0.63).

CONCLUSIONS

A radiomics signature allowed discrimination of nodal status with high diagnostic accuracy. The model based on manual LN segmentation outperformed the fully automated approach.

PATIENT SUMMARY

For patients with bladder cancer, evaluation of computed tomography (CT) scans before surgery using a computer-based method for image analysis, called radiomics, may help in standardizing and improving the accuracy of assessment of lymph nodes. This could be a valuable tool for optimizing treatment options.

Radiomics Analysis on Digital Breast Tomosynthesis: Preoperative Evaluation of Lymphovascular Invasion Status in Invasive Breast Cancer

RATIONALE AND OBJECTIVES

To develop a digital breast tomosynthesis (DBT)-based radiomics nomogram for preoperative evaluation of lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC).

MATERIALS AND METHODS

A total of 135 patients with pathologically confirmed IBC who underwent preoperative DBT from July 2018 to May 2020 were retrospectively enrolled and randomized into the training and validation sets. Radiomics feature extraction was performed on the volume of interest (VOI) manually outlined. A four-step algorithmic was applied to screen the features with the highest predictive power in the training set for constructing the radiomics signature and calculating the correspondent radiomics score (Rad-score). Logistic regression analyses were utilized to develop a combined radiomics model that incorporated the DBT-reported clinicoradiological semantic features and Rad-score, which was visualized as a radiomics nomogram.

RESULTS

The percentage of LVI-positive patients was 60.2% and 59.5% in the training and validation sets, respectively. The radiomics signature was constructed based on nine features selected from the 1218 radiomics features extracted. Higher Rad-score, maximum tumor diameter, and spiculate margin were independent risk factors for LVI. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity of the radiomics nomogram were 0.905, 72.7%, and 94.6% in the training set, and 0.835, 80.0%, and 76.5% in the validation set, respectively; this data was higher than models incorporating clinicoradiological semantic features alone or the radiomics signature in both sets.

CONCLUSION

Preoperative DBT-based combined radiomic nomogram could be a potential biomarker for LVI in patients with IBC.

Artificial intelligence in multi-parametric magnetic resonance imaging: A review

Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.