MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images

PURPOSE

This study aimed to assess tumor regression grade (TRG) in patients with rectal cancer after neoadjuvant chemoradiotherapy (NCRT) through a machine learning-based radiomics analysis using baseline T2-weighted magnetic resonance (MR) images.

MATERIALS AND METHODS

In total, 148 patients with locally advanced rectal cancer(T2-4 or N+) who underwent MR imaging at baseline and after chemoradiotherapy between January 2010 and May 2021 were included. A region of interest for each tumor mass was drawn by a radiologist on oblique axial T2-weighted images, and main features were selected using principal component analysis after dimension reduction among 116 radiomics and three clinical features. Among eight learning models that were used for prediction model development, the model showing best performance was selected. Treatment responses were classified as either good or poor based on the MR-assessed TRG (mrTRG) and pathologic TRG (pTRG). The model performance was assessed using the area under the receiver operating curve (AUROC) to classify the response group.

RESULTS

Approximately 49% of the patients were in the good response (GR) group based on mrTRG (73/148) and 26.9% based on pTRG (28/104). The AUCs of clinical data, radiomics models, and combined radiomics with clinical data model for predicting mrTRG were 0.80 (95% confidence interval [CI] 0.73, 0.87), 0.74 (95% CI 0.66, 0.81), and 0.75(95% CI 0.68, 0.82), and those for predicting pTRG was 0.62 (95% CI 0.52, 0.71), 0.74 (95% CI 0.65, 0.82), and 0.79 (95% CI 0.71, 0.87).

CONCLUSION

Radiomics combined with clinical data model using baseline T2-weighted MR images demonstrated feasible diagnostic performance in predicting both MR-assessed and pathologic treatment response in patients with rectal cancer after NCRT.

Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study

Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10-4). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10-3). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10-3) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications.

Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

OBJECTIVES

To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model.

MATERIALS AND METHODS

This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors' region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models' overall performance.

RESULTS

The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481-0.868), CEUS 0.821 (95%CI: 0.660-0.983), Combined_1 0.829 (95%CI: 0.673-0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509-0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P < 0.01). Both univariate and multivariate logistic regression analyses showed that age (P < 0.01) and clinical stage (P < 0.01) could be an independent predictor of efficacy after nCRT in patients with LARC.

CONCLUSION

The ultrasomics model had better diagnostic performance to predict efficacy to nCRT in patients with LARC than the Clinical model.

Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities

BACKGROUND

Deep learning methods have shown great potential in processing multi-modal Magnetic Resonance Imaging (MRI) data, enabling improved accuracy in brain tumor segmentation. However, the performance of these methods can suffer when dealing with incomplete modalities, which is a common issue in clinical practice. Existing solutions, such as missing modality synthesis, knowledge distillation, and architecture-based methods, suffer from drawbacks such as long training times, high model complexity, and poor scalability.

METHOD

This paper proposes IMS2Trans, a novel lightweight scalable Swin Transformer network by utilizing a single encoder to extract latent feature maps from all available modalities. This unified feature extraction process enables efficient information sharing and fusion among the modalities, resulting in efficiency without compromising segmentation performance even in the presence of missing modalities.

RESULTS

Two datasets, BraTS 2018 and BraTS 2020, containing incomplete modalities for brain tumor segmentation are evaluated against popular benchmarks. On the BraTS 2018 dataset, our model achieved higher average Dice similarity coefficient (DSC) scores for the whole tumor, tumor core, and enhancing tumor regions (86.57, 75.67, and 58.28, respectively), in comparison with a state-of-the-art model, i.e. mmFormer (86.45, 75.51, and 57.79, respectively). Similarly, on the BraTS 2020 dataset, our model scored higher DSC scores in these three brain tumor regions (87.33, 79.09, and 62.11, respectively) compared to mmFormer (86.17, 78.34, and 60.36, respectively). We also conducted a Wilcoxon test on the experimental results, and the generated p-value confirmed that our model's performance was statistically significant. Moreover, our model exhibits significantly reduced complexity with only 4.47 M parameters, 121.89G FLOPs, and a model size of 77.13 MB, whereas mmFormer comprises 34.96 M parameters, 265.79 G FLOPs, and a model size of 559.74 MB. These indicate our model, being light-weighted with significantly reduced parameters, is still able to achieve better performance than a state-of-the-art model.

CONCLUSION

By leveraging a single encoder for processing the available modalities, IMS2Trans offers notable scalability advantages over methods that rely on multiple encoders. This streamlined approach eliminates the need for maintaining separate encoders for each modality, resulting in a lightweight and scalable network architecture. The source code of IMS2Trans and the associated weights are both publicly available at https://github.com/hudscomdz/IMS2Trans.

Prediction of Pathologic Complete Response for Rectal Cancer Based on Pretreatment Factors Using Machine Learning

BACKGROUND

Pathologic complete response after neoadjuvant therapy is an important prognostic indicator for locally advanced rectal cancer and may give insights into which patients might be treated nonoperatively in the future. Existing models for predicting pathologic complete response in the pretreatment setting are limited by small data sets and low accuracy.

OBJECTIVE

We sought to use machine learning to develop a more generalizable predictive model for pathologic complete response for locally advanced rectal cancer.

DESIGN

Patients with locally advanced rectal cancer who underwent neoadjuvant therapy followed by surgical resection were identified in the National Cancer Database from years 2010 to 2019 and were split into training, validation, and test sets. Machine learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using an area under the receiver operating characteristic curve.

SETTINGS

This study used a national, multicenter data set.

PATIENTS

Patients with locally advanced rectal cancer who underwent neoadjuvant therapy and proctectomy.

MAIN OUTCOME MEASURES

Pathologic complete response defined as T0/xN0/x.

RESULTS

The data set included 53,684 patients. Pathologic complete response was experienced by 22.9% of patients. Gradient boosting showed the best performance with an area under the receiver operating characteristic curve of 0.777 (95% CI, 0.773-0.781), compared with 0.684 (95% CI, 0.68-0.688) for logistic regression. The strongest predictors of pathologic complete response were no lymphovascular invasion, no perineural invasion, lower CEA, smaller size of tumor, and microsatellite stability. A concise model including the top 5 variables showed preserved performance.

LIMITATIONS

The models were not externally validated.

CONCLUSIONS

Machine learning techniques can be used to accurately predict pathologic complete response for locally advanced rectal cancer in the pretreatment setting. After fine-tuning a data set including patients treated nonoperatively, these models could help clinicians identify the appropriate candidates for a watch-and-wait strategy. See Video Abstract .

EL CNCER DE RECTO BASADA EN FACTORES PREVIOS AL TRATAMIENTO MEDIANTE EL APRENDIZAJE AUTOMTICO

ANTECEDENTES:La respuesta patológica completa después de la terapia neoadyuvante es un indicador pronóstico importante para el cáncer de recto localmente avanzado y puede dar información sobre qué pacientes podrían ser tratados de forma no quirúrgica en el futuro. Los modelos existentes para predecir la respuesta patológica completa en el entorno previo al tratamiento están limitados por conjuntos de datos pequeños y baja precisión.OBJETIVO:Intentamos utilizar el aprendizaje automático para desarrollar un modelo predictivo más generalizable para la respuesta patológica completa para el cáncer de recto localmente avanzado.DISEÑO:Los pacientes con cáncer de recto localmente avanzado que se sometieron a terapia neoadyuvante seguida de resección quirúrgica se identificaron en la Base de Datos Nacional del Cáncer de los años 2010 a 2019 y se dividieron en conjuntos de capacitación, validación y prueba. Las técnicas de aprendizaje automático incluyeron bosque aleatorio, aumento de gradiente y red neuronal artificial. También se creó un modelo de regresión logística. El rendimiento del modelo se evaluó utilizando el área bajo la curva característica operativa del receptor.ÁMBITO:Este estudio utilizó un conjunto de datos nacional multicéntrico.PACIENTES:Pacientes con cáncer de recto localmente avanzado sometidos a terapia neoadyuvante y proctectomía.PRINCIPALES MEDIDAS DE VALORACIÓN:Respuesta patológica completa definida como T0/xN0/x.RESULTADOS:El conjunto de datos incluyó 53.684 pacientes. El 22,9% de los pacientes experimentaron una respuesta patológica completa. El refuerzo de gradiente mostró el mejor rendimiento con un área bajo la curva característica operativa del receptor de 0,777 (IC del 95%: 0,773 - 0,781), en comparación con 0,684 (IC del 95%: 0,68 - 0,688) para la regresión logística. Los predictores más fuertes de respuesta patológica completa fueron la ausencia de invasión linfovascular, la ausencia de invasión perineural, un CEA más bajo, un tamaño más pequeño del tumor y la estabilidad de los microsatélites. Un modelo conciso que incluye las cinco variables principales mostró un rendimiento preservado.LIMITACIONES:Los modelos no fueron validados externamente.CONCLUSIONES:Las técnicas de aprendizaje automático se pueden utilizar para predecir con precisión la respuesta patológica completa para el cáncer de recto localmente avanzado en el entorno previo al tratamiento. Después de realizar ajustes en un conjunto de datos que incluye pacientes tratados de forma no quirúrgica, estos modelos podrían ayudar a los médicos a identificar a los candidatos adecuados para una estrategia de observar y esperar. (Traducción-Dr. Ingrid Melo ).

T2WI-based MRI radiomics for the prediction of preoperative extranodal extension and prognosis in resectable rectal cancer

OBJECTIVE

To investigate whether T2-weighted imaging (T2WI)-based intratumoral and peritumoral radiomics can predict extranodal extension (ENE) and prognosis in patients with resectable rectal cancer.

METHODS

One hundred sixty-seven patients with resectable rectal cancer including T3T4N + cases were prospectively included. Radiomics features were extracted from intratumoral, peritumoral 3 mm, and peritumoral-mesorectal fat on T2WI images. Least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature score (Radscore) was built with logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each Radscore. A clinical-radiomics nomogram was constructed by the most predictive radiomics signature and clinical risk factors. A prognostic model was constructed by Cox regression analysis to identify 3-year recurrence-free survival (RFS).

RESULTS

Age, cT stage, and lymph node-irregular border and/or adjacent fat invasion were identified as independent clinical risk factors to construct a clinical model. The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and independent clinical risk factors achieved a better AUC than the clinical model in the training (0.799 vs. 0.736) and validation cohorts (0.723 vs. 0.667). Nomogram-based ENE (hazard ratio [HR] = 2.625, 95% CI = 1.233-5.586, p = 0.012) and extramural vascular invasion (EMVI) (HR = 2.523, 95% CI = 1.247-5.106, p = 0.010) were independent risk factors for predicting 3-year RFS. The prognostic model constructed by these two indicators showed good performance for predicting 3-year RFS in the training (AUC = 0.761) and validation cohorts (AUC = 0.710).

CONCLUSION

The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and clinical risk factors could predict preoperative ENE. Combining nomogram-based ENE and MRI-reported EMVI may be useful in predicting 3-year RFS.

CRITICAL RELEVANCE STATEMENT

A clinical-radiomics nomogram could help preoperative predict ENE, and a prognostic model constructed by the nomogram-based ENE and MRI-reported EMVI could predict 3-year RFS in patients with resectable rectal cancer.

KEY POINTS

• Intratumoral and peritumoral 3 mm Radscore showed the most capability for predicting ENE. • Clinical-radiomics nomogram achieved the best predictive performance for predicting ENE. • Combining clinical-radiomics based-ENE and EMVI showed good performance for 3-year RFS.

Utility of contrast-enhanced MRI radiomics features combined with clinical indicators for predicting induction chemotherapy response in primary central nervous system lymphoma

PURPOSE

To assess the utility of combining contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics features with clinical variables in predicting the response to induction chemotherapy (IC) for primary central nervous system lymphoma (PCNSL).

METHODS

A total of 131 patients with PCNSL (101 in the training set and 30 in the testing set) who had undergone contrast-enhanced MRI scans were retrospectively analyzed. Pyradiomics was utilized to extract radiomics features, and the clinical variables of the patients were gathered. Radiomics prediction models were developed using different combinations of feature selection methods and machine learning models, and the best combination was ultimately chosen. We screened clinical variables associated with treatment outcomes and developed clinical prediction models. The predictive performance of radiomics model, clinical model, and combined model, which integrates the best radiomics model and clinical characteristics, was independently assessed and compared using Receiver Operating Characteristic (ROC) curves.

RESULTS

In total, we extracted 1598 features. The best radiomics model we selected as the best utilized T-test and Recursive Feature Elimination (RFE) for feature selection and logistic regression for model building. Serum Interleukin 2 Receptor (IL-2R) and Eastern Cooperative Oncology Group (ECOG) Score were utilized to develop a clinical predictive model for assessing the response to induction chemotherapy. The results of the testing set revealed that the combined prediction model (radiomics and IL-2R) achieved the highest area under the ROC curve at 0.868 (0.683, 0.967), followed by the radiomics model at 0.857 (0.681, 0.957), and the clinical prediction model (IL-2R and ECOG) at 0.618 (0.413, 0.797). The combined model was significantly more accurate than the clinical model, with an AUC of 0.868 compared to 0.618 (P < 0.05). While the radiomics model had slightly better predictive power than the clinical model, this difference was not statistically significant (AUC, 0.857 vs. 0.618, P > 0.05).

CONCLUSIONS

Our prediction model, which combines radiomics signatures from CE-MRI with serum IL-2R, can effectively stratify patients with PCNSL before high-dose methotrexate (HD-MTX) -based chemotherapy.

Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features

Purpose

We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats.

Methods

A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment.

Results

Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level.

Conclusions

MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.

An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study

The classification of extramural vascular invasion status using baseline magnetic resonance imaging in rectal cancer has gained significant attention as it is an important prognostic marker. Also, the accurate prediction of patients achieving complete response with primary staging MRI assists clinicians in determining subsequent treatment plans. Most studies utilised radiomics-based methods, requiring manually annotated segmentation and handcrafted features, which tend to generalise poorly. We retrospectively collected 509 patients from 9 centres, and proposed a fully automated pipeline for EMVI status classification and CR prediction with diffusion weighted imaging and T2-weighted imaging. We applied nnUNet, a self-configuring deep learning model, for tumour segmentation and employed learned multiple-level image features to train classification models, named MLNet. This ensures a more comprehensive representation of the tumour features, in terms of both fine-grained detail and global context. On external validation, MLNet, yielding similar AUCs as internal validation, outperformed 3D ResNet10, a deep neural network with ten layers designed for analysing spatiotemporal data, in both CR and EMVI tasks. For CR prediction, MLNet showed better results than the current state-of-the-art model using imaging and clinical features in the same external cohort. Our study demonstrated that incorporating multi-level image representations learned by a deep learning based tumour segmentation model on primary MRI improves the results of EMVI classification and CR prediction with good generalisation to external data. We observed variations in the contributions of individual feature maps to different classification tasks. This pipeline has the potential to be applied in clinical settings, particularly for EMVI classification.

KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients

BACKGROUND

Mutated KRAS may indicate an invasive nature and predict prognosis in locally advanced rectal cancer (LARC). We aimed to establish a radiomic model using pretreatment T2W MRIs to predict KRAS status and explore the association between the KRAS status or model predictions and lung metastasis.

METHODS

In this retrospective multicentre study, LARC patients from two institutions between January 2012 and January 2019 were randomly divided into training and testing cohorts. Least absolute shrinkage and selection operator (LASSO) regression and the support vector machine (SVM) classifier were utilized to select significant radiomic features and establish a prediction model, which was validated by radiomic score distribution and decision curve analysis. The association between the model stratification and lung metastasis was investigated by Cox regression and Kaplan‒Meier survival analysis; the results were compared by the log-rank test.

RESULTS

Overall, 103 patients were enrolled (73 and 30 in the training and testing cohorts, respectively). The median follow-up was 38.1 months (interquartile range: 26.9, 49.4). The radiomic model had an area under the curve (AUC) of 0.983 in the training cohort and 0.814 in the testing cohort. Using a cut-off of 0.679 defined by the receiver operating characteristic (ROC) curve, patients with a high radiomic score (RS) had a higher risk for lung metastasis (HR 3.565, 95% CI 1.337, 9.505, p = 0.011), showing similar predictive performances for the mutant and wild-type KRAS groups (HR 3.225, 95% CI 1.249, 8.323, p = 0.016, IDI: 1.08%, p = 0.687; NRI 2.23%, p = 0.766).

CONCLUSIONS

We established and validated a radiomic model for predicting KRAS status in LARC. Patients with high RS experienced more lung metastases. The model could noninvasively detect KRAS status and may help individualize clinical decision-making.

Prediction of Ureteral Injury During Colorectal Surgery Using Machine Learning

BACKGROUND

Ureteral injury (UI) is a rare but devastating complication during colorectal surgery. Ureteral stents may reduce UI but carry risks themselves. Risk predictors for UI could help target the use of stents, but previous efforts have relied on logistic regression (LR), shown moderate accuracy, and used intraoperative variables. We sought to use an emerging approach in predictive analytics, machine learning, to create a model for UI.

METHODS

Patients who underwent colorectal surgery were identified in the National Surgical Quality Improvement Program (NSQIP) database. Patients were split into training, validation, and test sets. The primary outcome was UI. Three machine learning approaches were tested including random forest (RF), gradient boosting (XGB), and neural networks (NN), and compared with traditional LR. Model performance was assessed using area under the curve (AUROC).

RESULTS

The data set included 262,923 patients, of whom 1519 (.578%) experienced UI. Of the modeling techniques, XGB performed the best, with an AUROC score of .774 (95% CI .742-.807) compared with .698 (95% CI .664-.733) for LR. Random forest and NN performed similarly with scores of .738 and .763, respectively. Type of procedure, work RVUs, indication for surgery, and mechanical bowel prep showed the strongest influence on model predictions.

CONCLUSIONS

Machine learning-based models significantly outperformed LR and previous models and showed high accuracy in predicting UI during colorectal surgery. With proper validation, they could be used to support decision making regarding the placement of ureteral stents preoperatively.

MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study

OBJECTIVE

To build T2WI-based multiregional radiomics for predicting tumor deposit (TD) and prognosis in patients with resectable rectal cancer.

MATERIALS AND METHODS

A total of 208 patients with pathologically confirmed rectal cancer from two hospitals were prospectively enrolled. Intra- and peritumoral features were extracted separately from T2WI images and the least absolute shrinkage and selection operator was used to screen the most valuable radiomics features. Clinical-radiomics nomogram was developed by radiomics signatures and the most predictive clinical parameters. Prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis.

RESULTS

For TD, the area under the receiver operating characteristic curve (AUC) for intratumoral radiomics model was 0.956, 0.823, and 0.860 in the training cohort, test cohort, and external validation cohort, respectively. AUC for the peritumoral radiomics model was 0.929, 0.906, and 0.773 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for combined intra- and peritumoral radiomics model was 0.976, 0.918, and 0.874 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for clinical-radiomics nomogram was 0.989, 0.777, and 0.870 in the training cohort, test cohort, and external validation cohort, respectively. The prognostic model constructed by combining intra- and peritumoral radiomics signature score (radscore)-based TD and MRI-reported lymph nodes metastasis (LNM) indicated good performance for predicting 3-year RFS, with AUC of 0.824, 0.865, and 0.738 in the training cohort, test cohort and external validation cohort, respectively.

CONCLUSION

Combined intra- and peritumoral radiomics model showed good performance for predicting TD. Combining intra- and peritumoral radscore-based TD and MRI-reported LNM indicated the recurrence risk.

CLINICAL RELEVANCE STATEMENT

Combined intra- and peritumoral radiomics model could help accurately predict tumor deposits. Combining this predictive model-based tumor deposits with MRI-reported lymph node metastasis was associated with relapse risk of rectal cancer after surgery.

KEY POINTS

• Combined intra- and peritumoral radiomics model provided better diagnostic performance than that of intratumoral and peritumoral radiomics model alone for predicting TD in rectal cancer. • The predictive performance of the clinical-radiomics nomogram was not improved compared with the combined intra- and peritumoral radiomics model for predicting TD. • The prognostic model constructed by combining intra- and peritumoral radscore-based TD and MRI-reported LNM showed good performance for assessing 3-year RFS.

Improved effects of the b-value for 2000 sec/mm2 DWI on an accurate qualitative and quantitative assessment of rectal cancer

BACKGROUND AND STUDY OBJECTIVES

A higher b-value Diffusion-weighted imaging (DWI) would improve the contrast between cancerous and noncancerous tissue. Apparent diffusion coefficient (ADC)-histogram analysis is a method that can provide statistical data and quantitative information on tumor heterogeneity. This study aimed to compare two high b-values (1000 and 2000 sec/mm2) DWI in tumor detection and diagnostic performance in identifying early-stage tumor rectal cancer.

PATIENTS AND METHODS

This blinded and blinded retrospective study involved 56 patients with rectal cancer and 45 patients. Two radiologists evaluated the qualitative detection parameters and quantitative parameters of the ADC evaluated histogram and compared them between two DWI sequences (b-value for 1000 sec/mm2 and 2000 sec/mm2). The characteristic curves were used to assess diagnostic administration for the ADC histogram in discriminating early-stage tumors.

RESULTS

The b-value for 2000 sec/mm2 DWI significantly improved AUCs, sensitivity, specificity, and precision and decreased false-positive rate for detection compared to the b-value for 1000 sec/mm2 (p < 0.05). The mean and fifth percentile ADC value for stage I using the b-value for 1000 sec/mm2 DWI was significantly higher than stage ≥ II (p = 0.036II and 0.016 respectively), as the well as fifth, 10th, mean ADC of the fifth, 10th, and 25th ADC percentile at b-value for 2000 sec/mm2 (p = 0.031, 0.014, 0.035 and 0.025 respectively). The AUCs of the fifth percentile ADC at b-value for 2000 sec/mm2 DWI in both readers in differentiating the stage Ⅰ tumor were the highest (0.732 and 0.751).

CONCLUSION

The b-value for 2000 sec/mm2 DWI could improve the accurate detection of rectal cancer. The fifth percentile ADC at b-value for 2000 sec/mm2 sec/mm2 DWI was more useful for discriminating early stage than the b-value for 1000 sec/mm2 DWI.

Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability

Image perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test-retest imaging on model reliability is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by the two methods using four different classifiers. A 191-patient public breast cancer dataset with 71 test-retest scans was used with pre-determined 117 training and 74 testing samples. We collected apparent diffusion coefficient images and manual tumor segmentations for radiomic feature extraction. Random translations, rotations, and contour randomizations were performed on the training images, and intra-class correlation coefficient (ICC) was used to filter high repeatable features. We evaluated model reliability in both internal generalizability and robustness, which were quantified by training and testing AUC and prediction ICC. Higher testing performance was found at higher feature ICC thresholds, but it dropped significantly at ICC = 0.95 for the test-retest model. Similar optimal reliability can be achieved with testing AUC = 0.7-0.8 and prediction ICC > 0.9 at the ICC threshold of 0.9. It is recommended to include feature repeatability analysis using image perturbation in any radiomic study when test-retest is not feasible, but care should be taken when deciding the optimal feature repeatability criteria.

Feasibility of high-resolution readout-segmented echo-planar imaging with simultaneous multislice imaging in assessing rectal cancer

PURPOSE

To investigate the feasibility of high-resolution readout-segmented echo-planar imaging (rs-EPI) with simultaneous multislice (SMS) imaging to predict well-differentiated rectal cancer.Kindly check and confirm whether the Author Name 'Hongyun Huang ' is correctly identified.confirm METHODS: A total of eighty-three patients with nonmucinous rectal adenocarcinoma received both prototype SMS high-spatial-resolution and conventional rs-EPI sequences. Image quality was subjectively assessed by two experienced radiologists using a 4-point Likert scale (1 = poor, 4 = excellent). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) of the lesion were measured by two experienced radiologists in the objective assessment. Paired t tests or Mann‒Whitney U tests were used to compare the two groups. The areas under the receiver operating characteristic (ROC) curves (AUCs) were used to determine the predictive value of the ADCs in discriminating well-differentiated rectal cancer in the two groups. A two-sided p value < 0.05 represented statistical significance.Please check and confirm if the authors and affiliation details have been correctly identified. Amend if necessary.confirm RESULTS: In the subjective assessment, high-resolution rs-EPI had better image quality than conventional rs-EPI (p < 0.001). High-resolution rs-EPI also had a significantly higher SNR and CNR (p < 0.001). The T stage of rectal cancer was inversely correlated with the ADCs measured on high-resolution rs-EPI (r = -0.622, p < 0.001) and rs-EPI (r = -0.567, p < 0.001). The AUC of high-resolution rs-EPI in predicting well-differentiated rectal cancer was 0.768.

CONCLUSION

High-resolution rs-EPI with SMS imaging provided significantly higher image quality, SNRs, and CNRs and more stable ADC measurements than conventional rs-EPI. Additionally, the pretreatment ADC on high-resolution rs-EPI could discriminate well-differentiated rectal cancer.

Baseline and early digital [ 18 F]FDG PET/CT and multiparametric MRI contain promising features to predict response to neoadjuvant therapy in locally advanced rectal cancer patients: a pilot study

OBJECTIVE

In this pilot study, we investigated the feasibility of response prediction using digital [ 18 F]FDG PET/computed tomography (CT) and multiparametric MRI before, during, and after neoadjuvant chemoradiation therapy in locally advanced rectal cancer (LARC) patients and aimed to select the most promising imaging modalities and timepoints for further investigation in a larger trial.

METHODS

Rectal cancer patients scheduled to undergo neoadjuvant chemoradiation therapy were prospectively included in this trial, and underwent multiparametric MRI and [ 18 F]FDG PET/CT before, 2 weeks into, and 6-8 weeks after chemoradiation therapy. Two groups were created based on pathological tumor regression grade, that is, good responders (TRG1-2) and poor responders (TRG3-5). Using binary logistic regression analysis with a cutoff value of P  ≤ 0.2, promising predictive features for response were selected.

RESULTS

Nineteen patients were included. Of these, 5 were good responders, and 14 were poor responders. Patient characteristics of these groups were similar at baseline. Fifty-seven features were extracted, of which 13 were found to be promising predictors of response. Baseline [T2: volume, diffusion-weighted imaging (DWI): apparent diffusion coefficient (ADC) mean, DWI: difference entropy], early response (T2: volume change, DWI: ADC mean change) and end-of-treatment presurgical evaluation MRI (T2: gray level nonuniformity, DWI: inverse difference normalized, DWI: gray level nonuniformity normalized), as well as baseline (metabolic tumor volume, total lesion glycolysis) and early response PET/CT (Δ maximum standardized uptake value, Δ peak standardized uptake value corrected for lean body mass), were promising features.

CONCLUSION

Both multiparametric MRI and [ 18 F]FDG PET/CT contain promising imaging features to predict response to neoadjuvant chemoradiotherapy in LARC patients. A future larger trial should investigate baseline, early response, and end-of-treatment presurgical evaluation MRI and baseline and early response PET/CT.

Radiomics from Mesorectal Blood Vessels and Lymph Nodes: A Novel Prognostic Predictor for Rectal Cancer with Neoadjuvant Therapy

The objective of our study is to investigate the predictive value of various combinations of radiomic features from intratumoral and different peritumoral regions of interest (ROIs) for achieving a good pathological response (pGR) following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study was conducted using data from LARC patients who underwent nCRT between 2013 and 2021. Patients were divided into training and validation cohorts at a ratio of 4:1. Intratumoral ROIs (ROI_ITU) were segmented on T2-weighted imaging, while peritumoral ROIs were segmented using two methods: ROI_{PTU_2mm}, ROI_{PTU_4mm}, and ROI_{PTU_6mm}, obtained by dilating the boundary of ROI_ITU by 2 mm, 4 mm, and 6 mm, respectively; and ROI_{MR_F} and ROI_{MR_BVLN}, obtained by separating the fat and blood vessels + lymph nodes in the mesorectum. After feature extraction and selection, 12 logistic regression models were established using radiomics features derived from different ROIs or ROI combinations, and five-fold cross-validation was performed. The average area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. The study included 209 patients, consisting of 118 pGR and 91 non-pGR patients. The model that integrated ROI_ITU and ROI_{MR_BVLN} features demonstrated the highest predictive ability, with an AUC (95% confidence interval) of 0.936 (0.904-0.972) in the training cohort and 0.859 (0.745-0.974) in the validation cohort. This model outperformed models that utilized ROI_ITU alone (AUC = 0.779), ROI_{MR_BVLN} alone (AUC = 0.758), and other models. The radscore derived from the optimal model can predict the treatment response and prognosis after nCRT. Our findings validated that the integration of intratumoral and peritumoral radiomic features, especially those associated with mesorectal blood vessels and lymph nodes, serves as a potent predictor of pGR to nCRT in patients with LARC. Pending further corroboration in future research, these insights could provide novel imaging markers for refining therapeutic strategies.

MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients

Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 - invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 - breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10-3). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 - breast cancer patients.

Diagnostic performance of magnetic resonance imaging in preoperative local staging of rectal cancer after neoadjuvant chemoradiotherapy

PURPOSE

This paper aims to investigate the diagnostic performance of magnetic resonance imaging (MRI) in predicting the pathologic stage of locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (CRT) and the role of MRI in selecting patients with a pathologic complete response (ypCR).

METHODS

Restaging MRI (yMRI) examinations of 136 patients with LARC treated with neoadjuvant CRT followed by surgery were retrospectively analyzed by two radiologists. All examinations were performed on a 1.5 Tesla MRI machine with a pelvic phased-array coil. T2-weighted turbo spin-echo images and diffusion-weighted imaging were obtained. Histopathologic reports of the surgical specimens were the reference standard. The accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV) of yMRI in predicting the pathologic T-stage (ypT), N-stage, and ypCR were calculated. The inter-observer agreement was evaluated using kappa statistics.

RESULTS

The yMRI results showed 67% accuracy, 59% sensitivity, 80% specificity, 81% PPV, and 56% NPV in identifying ypT (ypT0-2 versus ypT3-4). In predicting the nodal status, the yMRI results revealed 63% accuracy, 60% sensitivity, 65% specificity, 47% PPV, and 75% NPV. In predicting ypCR, the yMRI results showed 84% accuracy, 20% sensitivity, 92% specificity, 23% PPV, and 90% NPV. The kappa statistics revealed substantial agreement between the two radiologists.

CONCLUSION

Utilization of yMRI showed high specificity and PPV in predicting the tumor stage and high NPV in predicting the nodal stage; in addition, yMRI revealed moderate accuracy in the T and N classifications, mainly due to underestimating the tumor stage and overestimating the nodal status. Finally, yMRI revealed high specificity and NPV but low sensitivity in predicting the complete response.

Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?

In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer.

MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer

Purpose

To establish and evaluate multiregional T2-weighted imaging (T2WI)-based clinical-radiomics model for predicting lymph node metastasis (LNM) and prognosis in patients with resectable rectal cancer.

Methods

A total of 346 patients with pathologically confirmed rectal cancer from two hospitals between January 2019 and December 2021 were prospectively enrolled. Intra- and peritumoral features were extracted separately, and least absolute shrinkage and selection operator regression was applied for feature selection. Radiomics signatures were built using the selected features from different regions. The clinical-radiomic nomogram was developed by combining the intratumoral and peritumoral radiomics signatures score (radscore) and the most predictive clinical parameters. The diagnostic performances of the nomogram and clinical model were evaluated using the area under the receiver operating characteristic curve (AUC). The prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis.

Results

The intratumoral radscore (radscore 1) included four features, the peritumoral radscore (radscore 2) included five features, and the combined intratumoral and peritumoural radscore (radscore 3) included ten features. The AUCs for radscore 3 were higher than that of radscore 1 in training cohort (0.77 vs. 0.71, P=0.182) and internal validation cohort (0.76 vs. 0.64, P=0.041). The AUCs for radscore 3 were higher than that of radscore 2 in training cohort (0.77 vs. 0.74, P=0.215) and internal validation cohort (0.76 vs. 0.68, P=0.083). A clinical-radiomic nomogram showed a higher AUC compared with the clinical model in training cohort (0.84 vs. 0.67, P<0.001) and internal validation cohort (0.78 vs. 0.64, P=0.038) but not in external validation (0.72 vs. 0.76, P=0.164). Multivariate Cox analysis showed MRI-reported extramural vascular invasion (EMVI) (HR=1.099, 95%CI: 0.462-2.616; P=0.031) and clinical-radiomic nomogram-based LNM (HR=2.232, 95%CI:1.238-7.439; P=0.017) were independent risk factors for assessing 3-year RFS. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed good performance in training cohort (AUC=0.748), internal validation cohort (AUC=0.706) and external validation (AUC=0.688) for predicting 3-year RFS.

Conclusion

A clinical-radiomics nomogram exhibits good performance for predicting preoperative LNM. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed clinical potential for assessing 3-year RFS.

DWI-MR and PET-CT Functional Imaging for Boost Tumor Volume Delineation in Neoadjuvant Rectal Cancer Treatment

BACKGROUND/AIM

T2 weighted magnetic resonance (MR) imaging is the gold standard for locally advanced rectal cancer (LARC) staging. The potential benefit of functional imaging, as diffusion-weighted MR (DWI) and positron emission tomography-computed tomography (PET-CT), could be considered for treatment intensification strategies. Dose intensification resulted in better pathological complete response (pCR) rates. This study evaluated the inter-observer agreement between two radiation oncologists, and the difference in gross tumor volume (GTV) delineation in simulation-CT, T2-MR, DWI-MR, and PET-CT in patients with LARC.

PATIENTS AND METHODS

Two radiation oncologists prospectively delineated GTVs of 24 patients on simul-CT (CT_GTV), T2-weighted MR (T2_GTV), echo planar b1000 DWI (DWI_GTV) and PET-CT (PET_GTV). Observers' agreement was assessed using Dice index. Kruskal-Wallis test assessed differences between methods.

RESULTS

Mean CT_GTV, T2_GTV, DWI_GTV, and PET_GTV were 41.3±26.9 cc, 25.9±15.2 cc, 21±14.8 cc, and 37.7±27.7 cc for the first observer, and 42.2±27.9 cc, 27.6±16.9 cc, 19.9±14.9cc, and 34.8±24.3 cc for the second observer, respectively. Mean Dice index was 0.85 for CT_GTV, 0.84 for T2_GTV, 0.82 for DWI_GTV, and 0.89 for PET_GTV, representative of almost perfect agreement. Kruskal-Wallis test showed a statistically significant difference between methods (p=0.009). Dunn test showed there were differences between DWI_GTV vs. PET_GTV (p=0.040) and DWI_GTV vs. CT_GTV (p=0.008).

CONCLUSION

DWI resulted in smaller volume delineation compared to CT, T2-MR, and PET-CT functional images. Almost perfect agreements were reported for each imaging modality between two observers. DWI-MR seems to remain the optimal strategy for boost volume delineation for dose escalation in patients with LARC.

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

Age alone is not a barrier to efficacy of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for colorectal peritoneal metastases: analysis of 1138 patients from the UK and Ireland Colorectal Peritoneal Metastases Registry

BACKGROUND

The management of colorectal peritoneal metastases continues to be a challenge but recent evidence suggests cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) can improve survival. Uncertainty about the relationship between age and tumour biology makes patient selection challenging particularly when reported procedure related morbidity is high and impact on survival outcomes unknown. The UK and Ireland Colorectal Peritoneal Metastases Registry was reviewed to assess the influence of age on efficacy of CRS and HIPEC.

METHODS

A review of outcomes from the UK and Ireland Colorectal Peritoneal Metastases Registry was performed. Data from 2000 to 2021 were included from five centres in the UK and Ireland, and the cohort were sub-divided into three age groups; <45 years, 45-65 years and >65 years old. Primarily, we examined post-operative morbidity and survival outcomes across the three age groups. In addition, we examined the impact that the completeness of cytoreduction, nodal status, or adverse pathological features had on long-term survival.

RESULTS

During the study period, 1138 CPM patients underwent CRS HIPEC. 202 patients(17.8%) were <45 years, 549 patients(48.2%) aged 45-65 years and 387 patients(34%) >65 years. Overall, median length of surgery (CRS and HIPEC), median PCI score and rate of HIPEC administration was similar in all three groups, as was overall rates of major morbidity and/or mortality. Complete cytoreduction rates (CC0) were similar across the three cohorts; 77%, 80.6% and 81%, respectively. Median overall survival for all patients was 38 months following complete cytoreduction.

CONCLUSION

Age did not appear to influence morbidity or long-term survival following CRS and HIPEC. When complete cytoreduction is achieved survival outcomes are good. The addition of HIPEC can be performed safely and may reduce local recurrence within the peritoneum.

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Objectives

To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT).

Methods

Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC).

Results

There was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model.

Conclusion

MRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges

Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.

Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning

PURPOSE

The current study aimed to evaluate the association of endorectal ultrasound (EUS) radiomics features at different denoising filters based on machine learning algorithms and to predict radiotherapy response in locally advanced rectal cancer (LARC) patients.

METHODS

The EUS images of forty-three LARC patients, as a predictive biomarker for predicting the treatment response of neoadjuvant chemoradiotherapy (NCRT), were investigated. For despeckling, the EUS images were preprocessed by traditional filters (bilateral, wiener, lee, frost, median, and wavelet filters). The rectal tumors were delineated by two readers separately, and radiomics features were extracted. The least absolute shrinkage and selection operator were used for feature selection. Classifiers including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), random forest, naive Bayes, and decision tree were trained using stratified fivefold cross-validation for model development. The area under the curve (AUC) of the receiver operating characteristic curve followed by accuracy, precision, sensitivity, and specificity were obtained for model performance assessment.

RESULTS

The wavelet filter had the best results with means of AUC: 0.83, accuracy: 77.41%, precision: 82.15%, and sensitivity: 79.41%. LR and SVM by having AUC: 0.71 and 0.76; accuracy: 70.0% and 71.5%; precision: 75.0% and 73.0%; sensitivity: 69.8% and 80.2%; and specificity: 70.0% and 60.9% had the highest model's performance, respectively.

CONCLUSION

This study demonstrated that the EUS-based radiomics model could serve as pretreatment biomarkers in predicting pathologic features of rectal cancer. The wavelet filter and machine learning methods (LR and SVM) had good results on the EUS images of rectal cancer.

Impact of preoperative chemoradiation with higher dose intensity modulated radiotherapy on pathological complete response for locally advanced rectal cancer: a systematic review

INTRODUCTION

Neoadjuvant chemoradiation (CRT) followed by total mesorectal excision is the current standard-of-care for locally advanced UICC II-III stage rectal cancer (LARC). A pathological complete response (pCR) correlates with survival. Improvements of pCR, including dose escalation, should be explored. The aim of this explorative analysis is to assess the impact on pCR of intensity-modulated radiotherapy (IMRT) with simultaneous integrated boost (SIB).

AREAS COVERED

A literature search via PICO (Population, Intervention, Comparison, Outcome) in MEDLINE/PubMed and EMBASE and a systematic review according to PRISMA (Preferred Reporting Items for Systematic Reviews and Metanalysis) methodology were performed. Studies that reported pCR rate in patients with LARC in clinical stage T2N+M0 or cT3/4 N0/+M0 treated with preoperative CRT with SIB-IMRT/VMAT (Volumetric Modulated Arc Therapy) were included. Sixty-two studies were identified, but only eight clinical trials with a total of 311 patients were included . Median follow-up was 16-61 months. pCR reached the value of 38%. Good survival outcomes were observed with a mild toxicity profile.

EXPERT OPINION

Radiotherapy dose intensification in LARC showed a slight increase of pCR compared to historical studies. Prospective evaluations are necessary to define which patients would benefit most.

Artificial intelligence and machine learning in cancer imaging

An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.

Comparison of the diagnostic performance of changes in signal intensity and volume from multiparametric MRI for assessing response of rectal cancer to neoadjuvant chemoradiotherapy

AIM

To evaluate the change in signal intensity (SI) and volume (V) from multiparametric magnetic resonance imaging (MRI) for assessing the response of locally advanced rectal cancer (LARC) to chemoradiotherapy (CRT).

MATERIALS AND METHODS

Eight-two LARC patients who underwent pre- and post-CRT T2-weighted (T2W), apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted (ceT1W) MRI were retrospectively analyzed. The change of volume (%△V) and relative SI ratio (%△SIR) from each sequence were determined. All LARCs were confirmed pathologically and classified as tumor regression grade (TRG) -0, 1, 2,or 3. Descriptive statistics and receiver operating characteristic (ROC) analysis, with calculation of area under the curve (AUC), were used to compare the diagnostic performances.

RESULTS

Sixteen patients had TRG-0, 15 had TRG-1, 35 had TRG-2, and 16 had TRG-3. Except for ADC-%△SIR, the remaining %△V and %△SIR values on MR sequences had significant differences among the four groups. The %△V and %△SIR (alone or together) did not distinguish TRG-1 from TRG-2, nor TRG-2 from TRG-3; however, differences between other TRGs were identified by %△V and %△SIR. The combined use of ADC-%△V and T2W-%△SIR provided the best diagnostic performance in distinguishing of TRG-0 from TRG-2 (AUC: 0.954) and from TRG-3 (AUC: 1.000).

CONCLUSIONS

Preoperative MRI of LARC patients after CRT has high diagnostic value for determination TRG, and may therefore improve the selection of patients most suitable for surgery.

Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review

Simple Summary

The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers.

Abstract

Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.

Estimation of colon cancer grade and metastatic lymph node involvement using DWI/ADC sequences

BACKGROUND

The potential benefit of neoadjuvant chemotherapy (NAC) in colon cancer is under evaluation. There is a need to improve preoperative non-invasive diagnostics using techniques that provide more accurate staging information in assessing patient eligibility for NAC.

PURPOSE

To investigate the link between the tumor grade (pathohistological confirmed) and the N status (corresponding to lymph node involvement) with apparent diffusion coefficient (ADC) values.

MATERIAL AND METHODS

A total of 17 patients planned for surgical resection had a biopsy confirming colon carcinoma and participated in the study. Abdominal magnetic resonance imaging with diffusion-weighted imaging/ADC sequence was recorded before surgery. The tumor and all visible lymph nodes were manually delineated directly on a grayscale ADC map for every single slice and detected to access the total tumor and summarized lymph node volume. The mean ADC value was further calculated for the mean tumor and mean lymph node values.

RESULTS

Low-grade tumors had a mean ADC equivalent to 1225 ± 170×10^-6 mm²/s, and the coefficient of high-grade tumors was 1444 ± 69×10^-6 mm²/s. The group of patients with positive lymph nodes in operative tissue samples (N+) exhibited lower mean ADC values (1023 ± 142×10^-6 mm²/s) as opposed to the group without metastatic lymph nodes (N-) with ADC values of 1260 ± 231×10^-6 mm²/s.

CONCLUSION

The mean whole-tumor ADC is associated with the histological tumor grade, and the mean ADC value of whole-volume abdominal lymph nodes could assume real nodal infiltration.

MRI-Based Radiomics Features to Predict Treatment Response to Neoadjuvant Chemotherapy in Locally Advanced Rectal Cancer: A Single Center, Prospective Study

This is a prospective, single center study aimed to evaluate the predictive power of peritumor and intratumor radiomics features assessed using T2 weight image (T2WI) of baseline magnetic resonance imaging (MRI) in evaluating pathological good response to NAC in patients with LARC (including Tany N+ or T3/4a Nany but not T4b). In total, 137 patients with LARC received NAC between April 2014 and August 2020. All patients were undergoing contrast-enhanced MRI and 129 patients contained small field of view (sFOV) sequence which were performed prior to treatment. The tumor regression grade standard was based on pathological response. The training and validation sets (n=91 vs. n=46) were established by random allocation of the patients. Receiver operating characteristic curve (ROC) analysis was applied to estimate the performance of different models based on clinical characteristics and radiomics features obtained from MRI, including peritumor and intratumor features, in predicting treatment response; these effects were calculated using the area under the curve (AUC). The performance and agreement of the nomogram were estimated using calibration plots. In total, 24 patients (17.52%) achieved a complete or near-complete response. For the individual radiomics model in the validation set, the performance of peritumor radiomics model in predicting treatment response yield an AUC of 0.838, while that of intratumor radiomics model is 0.805, which show no statically significant difference between then(P>0.05). The traditional and selective clinical features model shows a poor predictive ability in treatment response (AUC=0.596 and 0.521) in validation set. The AUC of combined radiomics model was improved compared to that of the individual radiomics models in the validation sets (AUC=0.844). The combined clinic-radiomics model yield the highest AUC (0.871) in the validation set, although it did not improve the performance of the radiomics model for predicting treatment response statically (P>0.05). Good agreement and discrimination were observed in the nomogram predictions. Both peritumor and intratumor radiomics features performed similarly in predicting a good response to NAC in patients with LARC. The clinic-radiomics model showed the best performance in predicting treatment response.

An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer

Objective

To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery.

Methods

A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validation set (60 patients). We extracted the radiomic features from the largest gray ultrasound image of the RC lesion. The intraclass correlation coefficient (ICC) was applied to test the repeatability of the radiomic features. The least absolute shrinkage and selection operator (LASSO) was used to reduce the data dimension and select significant features. Logistic regression (LR) analysis was applied to establish the radiomics model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the comprehensive performance of the model.

Results

Among the 203 patients, 33 (16.7%) were LVI positive and 170 (83.7%) were LVI negative. A total of 5350 (90.1%) radiomic features with ICC values of ≥ 0.75 were reported, which were subsequently subjected to hypothesis testing and LASSO regression dimension reduction analysis. Finally, 15 selected features were used to construct the radiomics model. The area under the curve (AUC) of the training set was 0.849, and the AUC of the validation set was 0.781. The calibration curve indicated that the radiomics model had good calibration, and DCA demonstrated that the model had clinical benefits.

Conclusion

The proposed endorectal ultrasound-based radiomics model has the potential to predict LVI preoperatively in RC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00813-6.

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

Background

Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models.

Methods

Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers.

Results

Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation.

Conclusion

Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41747-022-00272-2.

Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer

Background

To report on the discriminative ability of a simulation Computed Tomography (CT)-based radiomics signature for predicting response to treatment in patients undergoing neoadjuvant chemo-radiation for locally advanced adenocarcinoma of the rectum.

Methods

Consecutive patients treated at the Universities of Tübingen (from 1/1/07 to 31/12/10, explorative cohort) and Florence (from 1/1/11 to 31/12/17, external validation cohort) were considered in our dual-institution, retrospective analysis. Long-course neoadjuvant chemo-radiation was performed according to local policy. On simulation CT, the rectal Gross Tumor Volume was manually segmented. A feature selection process was performed yielding mineable data through an in-house developed software (written in Python 3.6). Model selection and hyper-parametrization of the model was performed using a fivefold cross validation approach. The main outcome measure of the study was the rate of pathologic good response, defined as the sum of Tumor regression grade (TRG) 3 and 4 according to Dworak’s classification.

Results

Two-hundred and one patients were included in our analysis, of whom 126 (62.7%) and 75 (37.3%) cases represented the explorative and external validation cohorts, respectively. Patient characteristics were well balanced between the two groups. A similar rate of good response to neoadjuvant treatment was obtained in in both cohorts (46% and 54.7%, respectively; p = 0.247). A total of 1150 features were extracted from the planning scans. A 5-metafeature complex consisting of Principal component analysis (PCA)-clusters (whose main components are LHL Grey-Level-Size-Zone: Large Zone Emphasis, Elongation, HHH Intensity Histogram Mean, HLL Run-Length: Run Level Variance and HHH Co-occurence: Cluster Tendency) in combination with 5-nearest neighbour model was the most robust signature. When applied to the explorative cohort, the prediction of good response corresponded to an average Area under the curve (AUC) value of 0.65 ± 0.02. When the model was tested on the external validation cohort, it ensured a similar accuracy, with a slightly lower predictive ability (AUC of 0.63).

Conclusions

Radiomics-based, data-mining from simulation CT scans was shown to be feasible and reproducible in two independent cohorts, yielding fair accuracy in the prediction of response to neoadjuvant chemo-radiation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02053-y.

Preoperative prediction of extramural venous invasion in rectal cancer by dynamic contrast-enhanced and diffusion weighted MRI: a preliminary study

Background

To explore the value of the quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters in assessing preoperative extramural venous invasion (EMVI) in rectal cancer.

Methods

Eighty-two rectal adenocarcinoma patients who had underwent MRI preoperatively were enrolled in this study. The differences in quantitative DCE-MRI and DWI parameters including Krans, Kep and ADC values were analyzed between MR-detected EMVI (mrEMVI)-positive and -negative groups. Multivariate logistic regression analysis was performed to build the combined prediction model for pathologic EMVI (pEMVI) with statistically significant quantitative parameters. The performance of the model for predicting pEMVI was evaluated using receiver operating characteristic (ROC) curve.

Results

Of the 82 patients, 24 were mrEMVI-positive and 58 were -negative. In the mrEMVI positive group, the Ktrans and Kep values were significantly higher than those in the mrEMVI negative group (P < 0.01), but the ADC values were significantly lower (P < 0.01). A negative correlation was observed between the Ktrans vs ADC values and Kep vs ADC values in patients with rectal cancer. Among the four quantitative parameters, Ktrans and ADC value were independently associated with mrEMVI by multivariate logistic regression analysis. ROC analysis showed that combined prediction model based on quantitative DCE parameters and ADC values had a good prediction efficiency for pEMVI in rectal cancer.

Conclusion

The quantitative DCE-MRI parameters, Krans, Kep and ADC values play important role in predicting EMVI of rectal cancer, with Ktrans and ADC value being independent predictors of EMVI in rectal cancer.

MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer

Background

To analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC).

Methods

Baseline MRI and clinical characteristics with neoadjuvant treatment modalities at four centres were collected. Decision tree, support vector machine and five-fold cross-validation were applied for two non-imaging and three radiomics-based models’ development and validation.

Results

We finally included 674 patients. Pre-treatment CEA, T stage, and histologic grade were selected to generate two non-imaging models: C model (clinical baseline characteristics alone) and CT model (clinical baseline characteristics combining neoadjuvant treatment modalities). The prediction performance of both non-imaging models were poor. The MBR signatures comprising 30 selected radiomics features, the MBR signatures combining clinical baseline characteristics (CMBR), and the CMBR incorporating neoadjuvant treatment modalities (CTMBR) all showed good discrimination with mean AUCs of 0.7835, 0.7871 and 0.7916 in validation sets, respectively. The three radiomics-based models had insignificant discrimination in performance.

Conclusions

The performance of the radiomics-based models were superior to the non-imaging models. MBR signatures seemed to reflect LARC’s true nature more accurately than clinical parameters and helped identify patients who can undergo organ preservation strategies.

Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

PURPOSE

This study aims to further enhance a validated radiomics-based model for predicting pathologic complete response (pCR) after chemo‑radiotherapy in locally advanced rectal cancer (LARC) for use in clinical practice.

METHODS

A generalized linear model (GLM) to predict pCR in LARC patients previously trained in Europe and validated with an external inter-continental cohort (59 patients), was first examined with further 88 intercontinental patient datasets to assess its reproducibility; then new radiomics and clinical features, and validation methods were investigated to build a new model for enhancing the pCR prediction for patients admitted to our department. The patients were divided into training group (75%) and validation group (25%) according to their demographic. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to reduce the dimensionality of the extracted features of the training group and select the optimal ones; the performance of the reference GLM and enhanced models was compared through the area under curve (AUC) of the receiver operating characteristics.

RESULTS

The value of AUC of the reference model was 0.831 (95% CI, 0.701-0.961), and 0.828 (95% CI, 0.700-0.956) in the original and new validation cohorts, respectively, showing a reproducibility in the applicability of the GLM model. Eight features were found to be significant with LASSO and used to establish an enhanced model. The AUC of the enhanced model of 0.926 (95% CI, 0.859-0.993) for training, and 0.926 (95% CI, 0.767-1.00) for the validation group shows better performance than the reference model.

CONCLUSIONS

The GLM model shows good reproducibility in predicting pCR in LARC; the enhanced model has the potential to improve prediction accuracy and may be a candidate in clinical practice.

Contemporary Management of Locally Advanced and Recurrent Rectal Cancer: Views from the PelvEx Collaborative

Pelvic exenteration is a complex operation performed for locally advanced and recurrent pelvic cancers. The goal of surgery is to achieve clear margins, therefore identifying adjacent or involved organs, bone, muscle, nerves and/or vascular structures that may need resection. While these extensive resections are potentially curative, they can be associated with substantial morbidity. Recently, there has been a move to centralize care to specialized units, as this facilitates better multidisciplinary care input. Advancements in pelvic oncology and surgical innovation have redefined the boundaries of pelvic exenterative surgery. Combined with improved neoadjuvant therapies, advances in diagnostics, and better reconstructive techniques have provided quicker recovery and better quality of life outcomes, with improved survival This article provides highlights of the current management of advanced pelvic cancers in terms of surgical strategy and potential future developments.

Diagnostic Impact of Radiological Findings and Extracellular Vesicles: Are We Close to Radiovesicolomics?

Simple Summary

Over the years, diagnostic tests such as in radiology and flow cytometry have become more and more powerful in the constant struggle against different pathologies, some of which are life-threatening. The possibility of using these “weapons” in a conjugated manner could result in higher healing and prevention rates, and a decrease in late diagnosis diseases. Different correlations among pathologies, extracellular vesicles (EVs), and radiological findings were recently demonstrated by many authors. Together with the increasing importance of “omics” sciences, and artificial intelligence in this new century, the perspective of a new research field called “radiovesicolomics” could be the missing link, enabling a different approach to disease diagnosis and treatment.

Abstract

Currently, several pathologies have corresponding and specific diagnostic and therapeutic branches of interest focused on early and correct detection, as well as the best therapeutic approach. Radiology never ceases to develop newer technologies in order to give patients a clear, safe, early, and precise diagnosis; furthermore, in the last few years diagnostic imaging panoramas have been extended to the field of artificial intelligence (AI) and machine learning. On the other hand, clinical and laboratory tests, like flow cytometry and the techniques found in the “omics” sciences, aim to detect microscopic elements, like extracellular vesicles, with the highest specificity and sensibility for disease detection. If these scientific branches started to cooperate, playing a conjugated role in pathology diagnosis, what could be the results? Our review seeks to give a quick overview of recent state of the art research which investigates correlations between extracellular vesicles and the known radiological features useful for diagnosis.

Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer

PURPOSE

To investigate the value of multiparametric MRI-based radiomics on predicting response to nCRT in patients with rectal cancer.

METHODS

This study enrolled 193 patients with pathologically confirmed LARC who received nCRT treatment between Apr. 2014 and Jun. 2018. All patients underwent baseline T1-weighted (T1W), T2-weighted (T2W) and T2-weighted fat-suppression (T2FS) MRI scans before neoadjuvant chemoradiotherapy. Radiomics features were extracted and selected from the MRI data to establish the radiomics signature. Important clinical predictors were identified by Mann-Whitney U test and Chi-square test. The nomogram integrating the radiomics signature and important clinical predictors was constructed using multivariate logistic regression. Prediction capabilities of each model were assessed with receiver operating characteristic (ROC) curve analysis. Performance of the nomogram was evaluated by its calibration and potential clinical usefulness.

RESULTS

For the prediction of good response (GR) and pathologic complete response (pCR), the developed radiomics signature comprising 10 and 7 features, respectively, were significantly associated with the therapeutic response to nCRT. The nomogram incorporating the radiomics signature and important clinical predictors (CEA and CA19-9 for predicting GR; CEA, posttreatment length and posttreatment thickness for predicting pCR) achieved favorable prediction efficacy, with AUCs of 0.918 (95% confidence interval [CI]: 0.867-0.971, Sen = 0.972, Spe = 0.828) and 0.944 (95% CI: 0.891-0.997, Sen = 0.943, Spe = 0.828) in the training and validation cohort for predicting GR, respectively; with AUCs of 0.959 (95% CI: 0.927-0.991, Sen = 1.000, Spe = 0.833) and 0.912 (95% CI: 0.843-0.982, Sen = 1.000, Spe = 0.815) in the training and validation cohort for predicting pCR, respectively. Decision curve analysis confirmed potential clinical usefulness of our nomogram.

CONCLUSIONS

This study demonstrated that the MRI-based radiomics nomogram is predictive of response to nCRT and can be considered as a promising tool for facilitating treatment decision-making for patients with LARC.

Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer

Objectives

To investigate a previously developed radiomics-based biparametric magnetic resonance imaging (bpMRI) approach for discrimination of clinically significant peripheral zone prostate cancer (PZ csPCa) using multi-center, multi-vendor (McMv) and single-center, single-vendor (ScSv) datasets.

Methods

This study’s starting point was a previously developed ScSv algorithm for PZ csPCa whose performance was demonstrated in a single-center dataset. A McMv dataset was collected, and 262 PZ PCa lesions (9 centers, 2 vendors) were selected to identically develop a multi-center algorithm. The single-center algorithm was then applied to the multi-center dataset (single–multi-validation), and the McMv algorithm was applied to both the multi-center dataset (multi–multi-validation) and the previously used single-center dataset (multi–single-validation). The areas under the curve (AUCs) of the validations were compared using bootstrapping.

Results

Previously the single–single validation achieved an AUC of 0.82 (95% CI 0.71–0.92), a significant performance reduction of 27.2% compared to the single–multi-validation AUC of 0.59 (95% CI 0.51–0.68). The new multi-center model achieved a multi–multi-validation AUC of 0.75 (95% CI 0.64–0.84). Compared to the multi–single-validation AUC of 0.66 (95% CI 0.56–0.75), the performance did not decrease significantly (p value: 0.114). Bootstrapped comparison showed similar single-center performances and a significantly different multi-center performance (p values: 0.03, 0.012).

Conclusions

A single-center trained radiomics-based bpMRI model does not generalize to multi-center data. Multi-center trained radiomics-based bpMRI models do generalize, have equal single-center performance and perform better on multi-center data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01099-y.

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-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease

Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS β-weights of radiomics features, including the 5% features with the largest β-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden's test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden's index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10-7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients.