A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

A dynamic online nomogram predicting prostate cancer short-term prognosis based on 18F-PSMA-1007 PET/CT of periprostatic adipose tissue: a multicenter study

BACKGROUND

Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18F-PSMA-1007 PET/CT of PPAT.

METHODS

Data from 268 prostate cancer (PCa) patients who underwent 18F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA.

RESULTS

The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78-0.91), 0.77 (95% CI: 0.62-0.91) and 0.84 (95% CI: 0.70-0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram.

CONCLUSION

The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.

Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication

The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and quantitatively analyzing subvisual features in medical imaging. Within this context, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in PC risk stratification. A stepwise literature search of publications from 2017 to 2023 was performed. Of 23 articles on PSMA PET/CT-derived prostate radiomics, PC diagnosis, prediction of biopsy Gleason score (GS), prediction of adverse pathology, and treatment outcomes were the primary endpoints of 4 (17.4%), 5 (21.7%), 7 (30.4%), and 7 (30.4%) studies, respectively. In predicting PC diagnosis, PSMA PET/CT-derived models performed well, with receiver operator characteristic curve area under the curve (ROC-AUC) values of 0.85-0.925. Similarly, in the prediction of biopsy and surgical pathology results, ROC-AUC values had ranges of 0.719-0.84 and 0.84-0.95, respectively. Finally, prediction of recurrence, progression, or survival following treatment was explored in nine studies, with ROC-AUC ranging 0.698-0.90. Of the 23 studies included in this review, 2 (8.7%) included external validation. While explorations of PSMA PET/CT-derived radiomic models are immature in follow-up and experience, these results represent great potential for future investigation and exploration. Prior to consideration for clinical use, however, rigorous validation in feature reproducibility and biologic validation of radiomic signatures must be prioritized.

Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation

OBJECTIVES

Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction.

MATERIALS AND METHODS

Data from 100 patients who received fractionated R(C)T were collected. 39 patients received additional ICI therapy. Computed Tomography (CT), RT segmentation and dose data were extracted and physical doses were converted to 2-Gy equivalent doses (EQD2) to account for different fractionation schemes. Features were reduced using Pearson intercorrelation and the Boruta algorithm within 1000-fold bootstrapping. Six single (clinics, Dose Volume Histogram (DVH), ICI, chemotherapy, radiomics, dosiomics) and four combined models (radiomics + dosiomics, radiomics + DVH + Clinics, dosiomics + DVH + Clinics, radiomics + dosiomics + DVH + Clinics) were trained to predict PTP. Dose-based models were tested using physical dose and EQD2. Four ML-algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using 5-fold nested cross validation and Synthetic Minority Oversampling Technique (SMOTE) for resampling in R. Prediction was evaluated using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds.

RESULTS

The combined model of all features using EQD2 surpassed all other models (AUC = 0.77, Confidence Interval CI 0.76-0.78). DVH, clinical data and ICI therapy had minor impact on PTP prediction with AUC values between 0.42 and 0.57. All EQD2-based models outperformed models based on physical dose.

CONCLUSIONS

Radiomics + dosiomics based ML models combined with clinical and dosimetric models were found to be suited best for PTP prediction after R(C)T and could improve pre-treatment decision making. Different RT dose fractionation schemes should be considered for dose-based ML approaches.

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy

Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study's results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa.

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.

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 the application of computed tomography-based radiomics for preoperative prediction of unfavorable pathology in initial bladder cancer

OBJECTIVES

To construct and validate unfavorable pathology (UFP) prediction models for patients with the first diagnosis of bladder cancer (initial BLCA) and to compare the comprehensive predictive performance of these models.

MATERIALS AND METHODS

A total of 105 patients with initial BLCA were included and randomly enrolled into the training and testing cohorts in a 7:3 ratio. The clinical model was constructed using independent UFP-risk factors determined by multivariate logistic regression (LR) analysis in the training cohort. Radiomics features were extracted from manually segmented regions of interest in computed tomography (CT) images. The optimal CT-based radiomics features to predict UFP were determined by the optimal feature filter and the least absolute shrinkage and selection operator algorithm. The radiomics model consist with the optimal features was constructed by the best of the six machine learning filters. The clinic-radiomics model combined the clinical and radiomics models via LR. The area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive value, calibration curve and decision curve analysis were used to evaluate the predictive performance of the models.

RESULTS

Patients in the UFP group had a significantly older age (69.61 vs. 63.93 years, p = 0.034), lager tumor size (45.7% vs. 11.1%, p = 0.002) and higher neutrophil to lymphocyte ratio (NLR; 2.76 vs. 2.33, p = 0.017) than favorable pathologic group in the training cohort. Tumor size (OR, 6.02; 95% CI, 1.50-24.10; p = 0.011) and NLR (OR, 1.50; 95% CI, 1.05-2.16; p = 0.026) were identified as independent predictive factors for UFP, and the clinical model was constructed using these factors. The LR classifier with the best AUC (0.817, the testing cohorts) was used to construct the radiomics model based on the optimal radiomics features. Finally, the clinic-radiomics model was developed by combining the clinical and radiomics models using LR. After comparison, the clinic-radiomics model had the best performance in comprehensive predictive efficacy (accuracy = 0.750, AUC = 0.817, the testing cohorts) and clinical net benefit among UFP-prediction models, while the clinical model (accuracy = 0.625, AUC = 0.742, the testing cohorts) was the worst.

CONCLUSION

Our study demonstrates that the clinic-radiomics model exhibits the best predictive efficacy and clinical net benefit for predicting UFP in initial BLCA compared with the clinical and radiomics model. The integration of radiomics features significantly improves the comprehensive performance of the clinical model.

Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy

PURPOSE

To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET).

MATERIAL AND METHODS

Consecutive patients, who underwent 68Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach.

RESULTS

Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature.

CONCLUSION

This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.

Optimal 18F-FDG PET/CT radiomics model development for predicting EGFR mutation status and prognosis in lung adenocarcinoma: a multicentric study

Purpose

To develop and interpret optimal predictive models to identify epidermal growth factor receptor (EGFR) mutation status and subtypes in patients with lung adenocarcinoma based on multicentric ¹⁸F-FDG PET/CT data, and further construct a prognostic model to predict their clinical outcome.

Methods

The ¹⁸F-FDG PET/CT imaging and clinical characters of 767 patients with lung adenocarcinoma from 4 cohorts were collected. Seventy-six radiomics candidates using cross-combination method to identity EGFR mutation status and subtypes were built. Further, Shapley additive explanations and local interpretable model-agnostic explanations were used for optimal models' interpretation. Moreover, in order to predict the overall survival, a multivariate Cox proportional hazard model based on handcrafted radiomics features and clinical characteristics was constructed. The predictive performance and clinical net benefit of the models were evaluated via area under receiver operating characteristic (AUC), C-index and decision curve analysis.

Results

Among the 76 radiomics candidates, light gradient boosting machine classifier (LGBM) combined with recursive feature elimination wrapped LGBM feature selection method achieved best performance in predicting EGFR mutation status (AUC reached 0.80, 0.61, 0.71 in the internal test cohort and two external test cohorts, respectively). And extreme gradient boosting classifier combined with support vector machine feature selection method achieved best performance in predicting EGFR subtypes (AUC reached 0.76, 0.63, 0.61 in the internal test cohort and two external test cohorts, respectively). The C-index of the Cox proportional hazard model achieved 0.863.

Conclusions

The integration of cross-combination method and the external validation from multi-center data achieved a good prediction and generalization performance in predicting EGFR mutation status and its subtypes. The combination of handcrafted radiomics features and clinical factors achieved good performance in predicting prognosis. With the urgent needs of multicentric ¹⁸F-FDG PET/CT trails, robust and explainable radiomics models have great potential in decision making and prognosis prediction of lung adenocarcinoma.

Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition

Introduction

Pneumonitis is a relevant side effect after radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICIs). Since the effect is radiation dose dependent, the risk increases for high fractional doses as applied for stereotactic body radiation therapy (SBRT) and might even be enhanced for the combination of SBRT with ICI therapy. Hence, patient individual pre-treatment prediction of post-treatment pneumonitis (PTP) might be able to support clinical decision making. Dosimetric factors, however, use limited information and, thus, cannot exploit the full potential of pneumonitis prediction.

Methods

We investigated dosiomics and radiomics model based approaches for PTP prediction after thoracic SBRT with and without ICI therapy. To overcome potential influences of different fractionation schemes, we converted physical doses to 2 Gy equivalent doses (EQD2) and compared both results. In total, four single feature models (dosiomics, radiomics, dosimetric, clinical factors) were tested and five combinations of those (dosimetric+clinical factors, dosiomics+radiomics, dosiomics+dosimetric+clinical factors, radiomics+dosimetric+clinical factors, radiomics+dosiomics+dosimetric+clinical factors). After feature extraction, a feature reduction was performed using pearson intercorrelation coefficient and the Boruta algorithm within 1000-fold bootstrapping runs. Four different machine learning models and the combination of those were trained and tested within 100 iterations of 5-fold nested cross validation.

Results

Results were analysed using the area under the receiver operating characteristic curve (AUC). We found the combination of dosiomics and radiomics features to outperform all other models with AUCradiomics+dosiomics, D = 0.79 (95% confidence interval 0.78-0.80) and AUCradiomics+dosiomics, EQD2 = 0.77 (0.76-0.78) for physical dose and EQD2, respectively. ICI therapy did not impact the prediction result (AUC ≤ 0.5). Clinical and dosimetric features for the total lung did not improve the prediction outcome.

Conclusion

Our results suggest that combined dosiomics and radiomics analysis can improve PTP prediction in patients treated with lung SBRT. We conclude that pre-treatment prediction could support clinical decision making on an individual patient basis with or without ICI therapy.

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.

Feasibility of a CT-based lymph node radiomics nomogram in detecting lymph node metastasis in PDAC patients

Objectives

To investigate the potential value of a contrast enhanced computed tomography (CECT)-based radiological-radiomics nomogram combining a lymph node (LN) radiomics signature and LNs’ radiological features for preoperative detection of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC).

Materials and methods

In this retrospective study, 196 LNs in 61 PDAC patients were enrolled and divided into the training (137 LNs) and validation (59 LNs) cohorts. Radiomic features were extracted from portal venous phase images of LNs. The least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation was used to select optimal features to determine the radiomics score (Rad-score). The radiological-radiomics nomogram was developed by using significant predictors of LN metastasis by multivariate logistic regression (LR) analysis in the training cohort and validated in the validation cohort independently. Its diagnostic performance was assessed by receiver operating characteristic curve (ROC), decision curve (DCA) and calibration curve analyses.

Results

The radiological model, including LN size, and margin and enhancement pattern (three significant predictors), exhibited areas under the curves (AUCs) of 0.831 and 0.756 in the training and validation cohorts, respectively. Nine radiomic features were used to construct a radiomics model, which showed AUCs of 0.879 and 0.804 in the training and validation cohorts, respectively. The radiological-radiomics nomogram, which incorporated the LN Rad-score and the three LNs’ radiological features, performed better than the Rad-score and radiological models individually, with AUCs of 0.937 and 0.851 in the training and validation cohorts, respectively. Calibration curve analysis and DCA revealed that the radiological-radiomics nomogram showed satisfactory consistency and the highest net benefit for preoperative diagnosis of LN metastasis.

Conclusions

The CT-based LN radiological-radiomics nomogram may serve as a valid and convenient computer-aided tool for personalized risk assessment of LN metastasis and help clinicians make appropriate clinical decisions for PADC patients.

Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning

Purpose

To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC).

Methods

Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort, n = 216; test cohort, n = 92). We extracted 207 handcrafted radiomic features and 1000 deep radiomic features of lymph nodes from their computed tomography (CT) images. The t-test and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimensions and select key features. Handcrafted radiomics, deep radiomics, and clinical features were combined to construct models. Models I (handcrafted radiomic features), II (Model I plus deep radiomic features), and III (Model II plus clinical features) were built using three machine learning methods: support vector machine (SVM), adaptive boosting (AdaBoost), and random forest (RF). The best model was compared with the results of two radiologists, and its performance was evaluated in terms of sensitivity, specificity, accuracy, area under the curve (AUC), and receiver operating characteristic (ROC) curve analysis.

Results

No significant differences were observed between cohorts. Ten handcrafted and 12 deep radiomic features were selected from the extracted features (p < 0.05). Model III could discriminate between patients with and without LNM better than the diagnostic results of the two radiologists.

Conclusion

The combination of handcrafted radiomic features, deep radiomic features, and clinical features could be used clinically to assess lymph node status in patients with ESCC.

Accuracy of 68Ga-PSMA PET-CT and PET-MRI in lymph node staging for localized prostate cancer

Objective

To evaluate the predictive value of positron emission computed tomography or magnetic resonance (PET-CT and PET-MRI) using gallium-68-labeled prostate-specific membrane antigen (⁶⁸Ga-PSMA) in lymph node involvement in prostate cancer.

Methods

A retrospective study comprising 91 patients diagnosed with prostate cancer between 2016 to 2020, who underwent ⁶⁸Ga-PSMA PET-CT or PET-MRI for staging before prostatectomy. The patients were divided into Group 1, with 65 patients with satisfactory pathological lymph node analysis, and Group 2, with 91 patients representing the sum of patients with pathological lymph node analysis and those with postoperative prostate-specific antigen within 60 days after surgery. Receiver Operating Characteristic curves were used to assess accuracy of predictive capacity of imaging exams for lymph node involvement.

Results

Regarding local clinical staging, the groups showed similar results, and 50% were classified as staging T2a. The accuracy of ⁶⁸Ga-PSMA PET-CT for prostate cancer lymph node staging was 86.5% (95%CI 0.74-0.94; p=0.06), with a sensitivity of 58.3% and specificity of 95%. The accuracy of ⁶⁸Ga-PSMA PET-MRI was 84.6% (95%CI 0.69-0.94; p=0.09), with a sensitivity of 40% and specificity of 100%. Considering both ⁶⁸Ga-PSMA PET-CT and PET-MRI, the accuracy was 85.7% (95%CI 0.76-0.92; p=0.015), with sensitivity of 50% and specificity of 97%.

Conclusion

The imaging tests ⁶⁸Ga-PSMA PET-CT and PET-MRI were highly accurate to detect preoperative lymph node involvement, and could be useful tools to indicate the need for extended lymph node dissection during radical prostatectomy.

Radiomics in prostate cancer: an up-to-date review

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

State of the art of radiomic analysis in the clinical management of prostate cancer: A systematic review

We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: "(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)" have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension. Seventy-six studies were included for qualitative analyses. Classifiers powered with radiomic features were able to discriminate between healthy tissue and PCa and between low- and high-risk PCa. However, before radiomics can be proposed for clinical use its methods have to be standardized, and these first encouraging results need to be robustly replicated in large and independent cohorts.

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.

MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy

PURPOSE

In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radiomics") may be able to predict the pathological complete response (pCR).

METHODS

MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort.

RESULTS

The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression.

CONCLUSION

This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.

Evaluation of 68Ga-PSMA-11 PET-MRI in Patients with Advanced Prostate Cancer Receiving 177Lu-PSMA-617 Therapy: A Radiomics Analysis

¹⁷⁷Lutetium PSMA-617 (Lu-PSMA) therapy in patients with metastatic castration resistant prostate cancer (mCRPC) has gained visibility through the ongoing phase III trial. The data on prediction of therapy outcome and survival out of pretherapeutic imaging parameters is still sparse. In this study, the predictive and prognostic value of radiomic features from ⁶⁸Ga-PSMA-11 PET-MRI are analyzed. In total, 21 patients with mCRPC underwent ⁶⁸Ga-PSMA-11 PET-MRI before Lu-PSMA therapy. The PET-positive tumor volume was defined and transferred to whole-body T2-, T1- and contrast-enhanced T1-weighted MRI-sequences. The radiomic features from PET and MRI sequences were extracted by using a freely available software package. For selecting features that allow differentiation of biochemical response (PSA decrease > 50%), a stepwise dimension reduction was performed. Logistic regression models were fitted, and selected features were tested for their prognostic value (overall survival) in all patients. Eight patients achieved biochemical response after Lu-PSMA therapy. Ten independent radiomic features differentiated well between responders and non-responders. The logistic regression model, including the feature interquartile range from T2-weighted images, revealed the highest accuracy (AUC = 0.83) for the prediction of biochemical response after Lu-PSMA therapy. Within the final model, patients with a biochemical response (p = 0.003) and higher T2 interquartile range values in pre-therapeutic imaging (p = 0.038) survived significantly longer. This proof-of-concept study provides first evidence on a potential predictive and prognostic value of radiomic analysis of pretherapeutic ⁶⁸Ga-PSMA-11 PET-MRI before Lu-PSMA therapy.

Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies

Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

BACKGROUND

In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging.

METHODS

Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture.

RESULTS

The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions.

CONCLUSIONS

MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics

BACKGROUND

In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients' risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS).

METHODS

Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS.

RESULTS

ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification.

CONCLUSIONS

T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Simple Summary

Determination of human papillomavirus (HPV) status for oropharyngeal cancer patients depicts a essential diagnostic factor and is important for treatment decisions. Current histological methods are invasive, time consuming and costly. We tested the ability of deep learning models for HPV status testing based on routinely acquired diagnostic CT images. A network trained for sports video clip classification was modified and then fine tuned for HPV status prediction. In this way, very basic information about image structures is induced into the model before training is started, while still allowing for exploitation of full 3D information in the CT images. Usage of this approach helps the network to cope with a small number of training examples and makes it more robust. For comparison, two other models were trained, one not relying on a pre-training task and another one pre-trained on 2D Data. The pre-trained video model preformed best.

Abstract

Infection with the human papillomavirus (HPV) has been identified as a major risk factor for oropharyngeal cancer (OPC). HPV-related OPCs have been shown to be more radiosensitive and to have a reduced risk for cancer related death. Hence, the histological determination of HPV status of cancer patients depicts an essential diagnostic factor. We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets, we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine-tuned, such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases, with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet, the video pre-trained model performed best. Deep learning models are capable of CT image-based HPV status determination. Video based pre-training has the ability to improve training for 3D medical data, but further studies are needed for verification.