Quantitative imaging outperforms molecular markers when predicting response to chemoradiotherapy for rectal cancer

MRI in rectal cancer: An institutional real life analysis of technical parameters

INTRODUCTION

MRI plays a key role in the preoperative staging of rectal cancers and choice of neoadjuvant radiochemotherapy. Yet, the acquisition and interpretation of rectum magnetic resonance imaging (MRI) turn out to be unequal, impacting patients'care. The present study aims at evaluating the quality of the acquisition of technical parameters of the rectal MRI performed by comparing them according to the various guidelines.

METHODS

The medical MRI reports of all consecutive patients with locally advanced rectal cancer treated in a curative intent, by preoperative RCT and completion surgery were retrospectively reviewed over two periods (January 2010-December 2014 and January 2018 and December 2020) according to international 2012 and 2016 ESGAR and 2017 SAR MRI recommendation reports.

RESULTS

During the first period (69 MRI performed), 58% of these MRI abided by the recommendations and 75% of essential criteria could be found in 25.5% of MRI reportings. During the second period (73 MRI performed), the protocol used by 6.8% of MR images abided by the 2016 Society of Gastrointestinal and Abdominal Radiology (ESGAR) recommendations and 39.7% abided by the Society of Abdominal Radiology (SAR) recommendations. 75% of essential criteria could be found in 52.3% of MRI reportings and 90% of essential criteria could be found in 6.2% of MRI reportings.

DISCUSSION

In an era of increasing individualized patient care and conservative treatment focused on tumour response and prognostic factors, the present study showed that compliance to MRI protocols and reporting guidelines needs improving to upgrade patient care.

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas

BACKGROUND

We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC.

METHOD

We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort.

RESULTS

A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset.

CONCLUSIONS AND RELEVANCE

We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.

Predicting response to chemoradiotherapy in rectal cancer via visual morphologic assessment and staging on baseline MRI: a multicenter and multireader study

PURPOSE

Pre-treatment knowledge of the anticipated response of rectal tumors to neoadjuvant chemoradiotherapy (CRT) could help to further optimize the treatment. Van Griethuysen et al. proposed a visual 5-point confidence score to predict the likelihood of response on baseline MRI. Aim was to evaluate this score in a multicenter and multireader study setting and compare it to two simplified (4-point and 2-point) adaptations in terms of diagnostic performance, interobserver agreement (IOA), and reader preference.

METHODS

Twenty-two radiologists from 14 countries (5 MRI-experts,17 general/abdominal radiologists) retrospectively reviewed 90 baseline MRIs to estimate if patients would likely achieve a (near-)complete response (nCR); first using the 5-point score by van Griethuysen (1=highly unlikely to 5=highly likely to achieve nCR), second using a 4-point adaptation (with 1-point each for high-risk T-stage, obvious mesorectal fascia invasion, nodal involvement, and extramural vascular invasion), and third using a 2-point score (unlikely/likely to achieve nCR). Diagnostic performance was calculated using ROC curves and IOA using Krippendorf's alpha (α).

RESULTS

Areas under the ROC curve to predict the likelihood of a nCR were similar for the three methods (0.71-0.74). IOA was higher for the 5- and 4-point scores (α=0.55 and 0.57 versus 0.46 for the 2-point score) with best results for the MRI-experts (α=0.64-0.65). Most readers (55%) favored the 4-point score.

CONCLUSIONS

Visual morphologic assessment and staging methods can predict neoadjuvant treatment response with moderate-good performance. Compared to a previously published confidence-based scoring system, study readers preferred a simplified 4-point risk score based on high-risk T-stage, MRF involvement, nodal involvement, and EMVI.

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.

An artificial neural network-based radiomics model for predicting the radiotherapy response of advanced esophageal squamous cell carcinoma patients: a multicenter study

Radiotherapy benefits patients with advanced esophageal squamous cell carcinoma (ESCC) in terms of symptom relief and long-term survival. In contrast, a substantial proportion of ESCC patients have not benefited from radiotherapy. This study aimed to establish and validate an artificial neural network-based radiomics model for the pretreatment prediction of the radiotherapy response of advanced ESCC by using integrated data combined with feasible baseline characteristics of computed tomography. A total of 248 patients with advanced ESCC who underwent baseline CT and received radiotherapy were enrolled in this study and were analyzed by two types of radiomics models, machine learning and deep learning. As a result, the Att. Resnet50 pretrained network model indicated superior performance, with AUCs of 0.876, 0.802 and 0.732 in the training, internal validation, and external validation cohorts, respectively. Similarly, our Att. Resnet50 pretrained network model showed excellent calibration and significant clinical benefit according to the C index and decision curve analysis. Herein, a novel pretreatment radiomics model was established based on deep learning methods and could be used for radiotherapy response prediction in advanced ESCC patients, thus providing reliable evidence for therapeutic decision-making.

Predicting neoadjuvant chemoradiotherapy response with functional imaging and liquid biomarkers in locally advanced rectal cancer

INTRODUCTION

Non-invasive predictive quantitative biomarkers are required to guide treatment individualization in patients with locally advanced rectal cancer (LARC) in order to maximise therapeutic outcomes and minimise treatment toxicity. Magnetic resonance imaging (MRI), positron emission tomography (PET) and blood biomarkers have the potential to predict chemoradiotherapy (CRT) response in LARC.

AREAS COVERED

This review examines the value of functional imaging (MRI and PET) and liquid biomarkers (circulating tumor cells (CTCs) and circulating tumor nucleic acid (ctNA)) in the prediction of CRT response in LARC. Selected imaging and liquid biomarker studies are presented and the current status of the most promising imaging (apparent diffusion co-efficient (ADC), K^trans, SUV_max, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) and liquid biomarkers (circulating tumor cells (CTCs), circulating tumor nucleic acid (ctNA)) is discussed. The potential applications of imaging and liquid biomarkers for treatment stratification and a pathway to clinical translation are presented.

EXPERT OPINION

Functional imaging and liquid biomarkers provide novel ways of predicting CRT response. The clinical and technical validation of the most promising imaging and liquid biopsy biomarkers in multi-centre studies with harmonised acquisition techniques is required. This will enable clinical trials to investigate treatment escalation or de-escalation pathways in rectal cancer.

Nonoperative versus Operative Approach According to the Response to Neoadjuvant Chemoradiotherapy for Rectal Cancer: A Prospective Cohort Study

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Watch-and-wait patients after chemoradiotherapy for rectal cancer have good functional outcome.

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No survival differences were seen between patients undergoing surgery versus patients in a watch-and-wait protocol.

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There is a subset of patients that has initial favorable response but will recur with distant metastases afterwards.

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A previously published model predicting (near)-complete response could not be validated.

Purpose

To report on organ preservation following chemoradiotherapy (CRT) in a prospective cohort of locally advanced rectal cancer patients.

Methods and materials

Fifty-two patients received CRT. MRI and ¹⁸F-FDG-PET/CT were performed prior to CRT. Response assessment was done 6 and 12 weeks after CRT using digital rectal examination, MRI, ¹⁸F-FDG-PET/CT and endoscopy. For clinical complete response or minimal residual disease, a watch-and-wait (W&W) protocol was started.

Regrowth-free survival (ReFS), Total Mesorectal Excision-free disease-free survival, distant metastasis-free survival (DMFS) and overall survival (OS) were evaluated using Kaplan-Meier method. Functional outcome was compared with the Wilcoxon signed-rank test using EORTC QLQ-C30, MSKCC BFI, LARS and IIEF-5/FSFI-5 questionnaires. A previously developed prediction model performance was tested using receiver operating characteristic analysis.

Results

29/52 patients entered a W&W protocol. There was no difference in two-year DMFS (81.1 % vs 78.8 %, p = 0.82), two-year OS (96.4 % vs 100 %, p = 0.38) and two-year DFS (77.5 % vs 78.8 %, p = 0.87) between W&W patients and those who underwent surgery at 12 weeks after CRT. Two-year DMFS differed between W&W with local regrowth, W&W with sustained response and patients who had surgery (66.7 % vs 88.0 % vs 78.8 %; p = 0.04). At 6 and 12 months, W&W patients reported good QoL and bowel function. The model validation reached an AUC of 0.627.

Conclusion

Good functional outcome in patients with rectal cancer allocated to surveillance after CRT needs to be balanced against potentially worse DMFS in a subset of patients without sustained clinical complete response. Reliable prediction of patients eligible for surveillance programs needs further investigation.

A decade of multi-modality PET and MR imaging in abdominal oncology

OBJECTIVES

To investigate trends observed in a decade of published research on multimodality PET(/CT)+MR imaging in abdominal oncology, and to explore how these trends are reflected by the use of multimodality imaging performed at our institution.

METHODS

First, we performed a literature search (2009-2018) including all papers published on the multimodality combination of PET(/CT) and MRI in abdominal oncology. Retrieved papers were categorized according to a structured labelling system, including study design and outcome, cancer and lesion type under investigation and PET-tracer type. Results were analysed using descriptive statistics and evolutions over time were plotted graphically. Second, we performed a descriptive analysis of the numbers of MRI, PET/CT and multimodality PET/CT+MRI combinations (performed within a ≤14 days interval) performed during a similar time span at our institution.

RESULTS

Published research papers involving multimodality PET(/CT)+MRI combinations showed an impressive increase in numbers, both for retrospective combinations of PET/CT and MRI, as well as hybrid PET/MRI. Main areas of research included new PET-tracers, visual PET(/CT)+MRI assessment for staging, and (semi-)quantitative analysis of PET-parameters compared to or combined with MRI-parameters as predictive biomarkers. In line with literature, we also observed a vast increase in numbers of multimodality PET/CT+MRI imaging in our institutional data.

CONCLUSIONS

The tremendous increase in published literature on multimodality imaging, reflected by our institutional data, shows the continuously growing interest in comprehensive multivariable imaging evaluations to guide oncological practice.

ADVANCES IN KNOWLEDGE

The role of multimodality imaging in oncology is rapidly evolving. This paper summarizes the main applications and recent developments in multimodality imaging, with a specific focus on the combination of PET+MRI in abdominal oncology.

Value of PET imaging for radiation therapy

This comprehensive review written by experts in their field gives an overview on the current status of incorporating positron emission tomography (PET) into radiation treatment planning. Moreover, it highlights ongoing studies for treatment individualisation and per-treatment tumour response monitoring for various primary tumours. Novel tracers and image analysis methods are discussed. The authors believe this contribution to be of crucial value for experts in the field as well as for policy makers deciding on the reimbursement of this powerful imaging modality.

Value of PET imaging for radiation therapy

This comprehensive review written by experts in their field gives an overview on the current status of incorporating positron emission tomography (PET) into radiation treatment planning. Moreover, it highlights ongoing studies for treatment individualisation and per-treatment tumour response monitoring for various primary tumours. Novel tracers and image analysis methods are discussed. The authors believe this contribution to be of crucial value for experts in the field as well as for policy makers deciding on the reimbursement of this powerful imaging modality.

Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer

OBJECTIVE

To investigate whether quantifying local tumour heterogeneity has added benefit compared to global tumour features to predict response to chemoradiotherapy using pre-treatment multiparametric PET and MRI data.

METHODS

Sixty-one locally advanced rectal cancer patients treated with chemoradiotherapy and staged at baseline with MRI and FDG-PET/CT were retrospectively analyzed. Whole-tumour volumes were segmented on the MRI and PET/CT scans from which global tumour features (T2W_volume/T2W_entropy/ADC_mean/SUV_mean/TLG/CT_mean-HU) and local texture features (histogram features derived from local entropy/mean/standard deviation maps) were calculated. These respective feature sets were combined with clinical baseline parameters (e.g. age/gender/TN-stage) to build multivariable prediction models to predict a good (Mandard TRG1-2) versus poor (Mandard TRG3-5) response to chemoradiotherapy. Leave-one-out cross-validation (LOOCV) with bootstrapping was performed to estimate performance in an 'independent' dataset.

RESULTS

When using only imaging features, local texture features showed an AUC = 0.81 versus AUC = 0.74 for global tumour features. After internal cross-validation (LOOCV), AUC to predict a good response was the highest for the combination of clinical baseline variables + global tumour features (AUC = 0.83), compared to AUC = 0.79 for baseline + local texture and AUC = 0.76 for all combined (baseline + global + local texture).

CONCLUSION

In imaging-based prediction models, local texture analysis has potential added value compared to global tumour features to predict response. However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture analysis appears to be limited. The overall performance to predict response when combining baseline variables with quantitative imaging parameters is promising and warrants further research.

KEY POINTS

• Quantification of local tumour texture on pre-therapy FDG-PET/CT and MRI has potential added value compared to global tumour features to predict response to chemoradiotherapy in rectal cancer. • However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture over global tumour features is limited. • Predictive performance of our optimal model-combining clinical baseline variables with global quantitative tumour features-was encouraging (AUC 0.83), warranting further research in this direction on a larger scale.

MRI radiomics in the prediction of therapeutic response to neoadjuvant therapy for locoregionally advanced rectal cancer: a systematic review

Introduction: The standard of care for locoregionally advanced rectal cancer is neoadjuvant therapy (NA CRT) prior to surgery, of which 10-30% experience a complete pathologic response (pCR). There has been interest in using imaging features, also known as radiomics features, to predict pCR and potentially avoid surgery. This systematic review aims to describe the spectrum of MRI studies examining high-performing radiomic features that predict NA CRT response.Areas covered: This article reviews the use of pre-therapy MRI in predicting NA CRT response for patients with locoregionally advanced rectal cancer (T3/T4 and/or N1+). The primary outcome was to identify MRI radiomic studies; secondary outcomes included the power and the frequency of use of radiomic features.Expert opinion: Advanced models incorporating multiple radiomics categories appear to be the most promising. However, there is a need for standardization across studies with regards to; the definition of NA CRT response, imaging protocols, and radiomics features incorporated. Further studies are needed to validate current radiomics models and to fully ascertain the value of MRI radiomics in the response prediction for locoregionally advanced rectal cancer.

Comparison of 18F-FDG PET/CT and DW-MRI in assessment of neoadjuvant radiochemotherapy response in locally advanced rectal cancer patients

OBJECTIVE

Our aim is to evaluate if different metabolic parameters obtained by ¹⁸F-FDG PET/CT and diffusion weighted magnetic resonance imaging (DW-MRI) can aid in neoadjuvant radiochemotherapy (RCT) response assessment in locally advanced rectal cancer (LARC) patients.

METHODS

Out of 20 LARC patients, who were planned to receive neoadjuvant RCT, 19 were included in this prospective study. Patients had ¹⁸F-FDG PET/CT and DW-MRI at initial staging, interim (2 weeks after onset of RCT) and after completion of RCT (post-therapy). Standardized uptake value (SUV) parameters (SUVmax, SUVmean, SUVpeak, SULpeak), metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG) detected on PET images and apparent diffusion coefficient (ADC) values (for b=400 and b=1000s/mm²) obtained from DW-MRI were recorded. Postoperative tumor regression grade (TRG) was used as gold-standard, except for 2 patients who were under complete remission with non-operative management 19 months post-therapy and scored as responders.

RESULTS

On interim PET/CT, no significant difference was found among PET parameters between responders and non-responders, whereas post-therapy SUVmax, SUVpeak, MTV, SULpeak, TLG (P=0.02, P=0.014, P=0.025, P=0.007, P=0.02, respectively) and initial MTV (P=0.034) were significantly lower in responders. ADC response index (RI) was higher in responders (interim P=0.026; post-therapy: P=0.018) and ROC analysis revealed that a threshold of ADC RI>41.6% for interim MRI and >44.6% for post-therapy MRI had sensitivity and specificity of 75.0% and 90.9%, respectively.

CONCLUSIONS

While interim ¹⁸F-FDG PET/CT failed to predict therapy response during RCT, post-therapy PET could accurately differentiate responders. DW-MRI was found to be more promising in interim detection of RCT response.

Modern MR Imaging Technology in Rectal Cancer; There Is More Than Meets the Eye

MR imaging (MRI) is now part of the standard work up of patients with rectal cancer. Restaging MRI has been traditionally used to plan the surgical approach. Its role has recently increased and been adopted as a valuable tool to assist the clinical selection of clinical (near) complete responders for organ preserving treatment. Recently several studies have addressed new imaging biomarkers that combined with morphological provides a comprehensive picture of the tumor. Diffusion-weighted MRI (DWI) has entered the clinics and proven useful for response assessment after chemoradiotherapy. Other functional (quantitative) MRI technologies are on the horizon including artificial intelligence modeling. This narrative review provides an overview of recent advances in rectal cancer (re)staging by imaging with a specific focus on response prediction and evaluation of neoadjuvant treatment response. Furthermore, directions are given for future research.

CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma

Purpose

To determine the performance of CT-based radiomic features for noninvasive prediction of histopathologic features of tumor grade, extracapsular spread, perineural invasion, lymphovascular invasion, and human papillomavirus status in head and neck squamous cell carcinoma (HNSCC).

Materials and Methods

In this retrospective study, which was approved by the local institutional ethics committee, CT images and clinical data from patients with pathologically proven HNSCC from The Cancer Genome Atlas (n = 113) and an institutional test cohort (n = 71) were analyzed. A machine learning model was trained with 2131 extracted radiomic features to predict tumor histopathologic characteristics. In the model, principal component analysis was used for dimensionality reduction, and regularized regression was used for classification.

Results

The trained radiomic model demonstrated moderate capability of predicting HNSCC features. In the training cohort and the test cohort, the model achieved a mean area under the receiver operating characteristic curve (AUC) of 0.75 (95% confidence interval [CI]: 0.68, 0.81) and 0.66 (95% CI: 0.45, 0.84), respectively, for tumor grade; a mean AUC of 0.64 (95% CI: 0.55, 0.62) and 0.70 (95% CI: 0.47, 0.89), respectively, for perineural invasion; a mean AUC of 0.69 (95% CI: 0.56, 0.81) and 0.65 (95% CI: 0.38, 0.87), respectively, for lymphovascular invasion; a mean AUC of 0.77 (95% CI: 0.65, 0.88) and 0.67 (95% CI: 0.15, 0.80), respectively, for extracapsular spread; and a mean AUC of 0.71 (95% CI: 0.29, 1.0) and 0.80 (95% CI: 0.65, 0.92), respectively, for human papillomavirus status.

Conclusion

Radiomic CT models have the potential to predict characteristics typically identified on pathologic assessment of HNSCC.Supplemental material is available for this article.© RSNA, 2020.

Controversies in Rectal Cancer Treatment and Management

Incorporation of new treatment modalities has significantly increased the complexity of the treatment and management of rectal cancer, including perioperative therapy for local advanced disease and organ preservation for those with response to the preoperative treatment. This review may help practitioners better understand the rationale and selection.

Locally advanced rectal cancer: qualitative and quantitative evaluation of diffusion-weighted magnetic resonance imaging in restaging after neoadjuvant chemo-radiotherapy

PURPOSE

To determine the added value of qualitative and quantitative evaluation of diffusion-weighted magnetic resonance imaging (DWI) in locally advanced rectal cancer (LARC) restaging after neoadjuvant chemo-radiotherapy (CRT).

MATERIALS AND METHODS

A retrospective study was performed of 21 patients with LARC treated with CRT. All patients were evaluated with 1.5 T conventional magnetic resonance imaging (MRI) and DWI (0-1000 s/mm²) before starting therapy and after neoadjuvant CRT. All included patients underwent surgery after CRT: the histopathological evaluation of surgical specimens represented the reference standard for local staging after neoadjuvant therapy. The qualitative analysis was carried out by two operators in consensus, who reviewed the conventional MR image set [T1-weighted and T2-weighted morphological sequences + dynamic contrast-enhanced sequences (DCE)] and the combined set of conventional and DW images. For the quantitative analysis, the apparent diffusion coefficient (ADC) values were measured at each examination. For each lesion, the mean ADC value (ADCpre and ADCpost) and the ΔADC (ADCpost - ADCpre) were calculated, and values of the three groups of response [complete response (pCR), partial response (pPR), stable disease (pSD)] were compared.

RESULTS

In LARC restaging, conventional MRI showed a sensitivity of 80% and a specificity of 50%, with a total diagnostic capacity of 71.40%, while by adding DWI sensitivity increased to 100%, specificity to 67%, and total diagnostic capacity to 90.40%. ΔADC correlates with treatment response and a cutoff of 1.35 × 10^-3 mm²/s predicts the pCR with a sensitivity of 93.3% and a specificity of 83.3%.

CONCLUSIONS

Adding DWI to conventional sequences may improve MRI capability to evaluate tumor response to CRT. The quantitative DWI assessment is promising, but larger studies are required.

Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics

BACKGROUND

In well-responding patients to chemoradiotherapy for locally advanced rectal cancer (LARC), a watch-and-wait strategy can be considered. To implement organ-sparing strategies, accurate patient selection is needed. We investigate the use of MRI-based radiomics models to predict tumor response to improve patient selection.

MATERIALS AND METHODS

Models were developed in a cohort of 70 patients and validated in an external cohort of 55 patients. Patients received chemoradiation followed by surgery and underwent T2-weighted and diffusion-weighted MRI (DW-MRI) before and after chemoradiation. The outcome measure was (near-)complete pathological tumor response (ypT0-1N0). Tumor segmentation was done on T2-images and transferred to b800-images and ADC maps, after which quantitative and four semantic features were extracted. We combined features using principal component analysis and built models using LASSO regression analysis. The best models based on precision and performance were selected for validation.

RESULTS

21/70 patients (30%) achieved ypT0-1N0 in the development cohort versus 13/55 patients (24%) in the validation cohort. Three models (t2_dwi_pre_post, semantic_dwi_adc_pre, semantic_dwi_post) were identified with an area-under-the-curve (AUC) of 0.83 (95% CI 0.70-0.95), 0.86 (95% CI 0.75-0.98) and 0.84 (95% CI 0.75-0.94) respectively. Two models (t2_dwi_pre_post, semantic_dwi_post) validated well in the external cohort with AUCs of 0.83 (95% CI 0.70-0.95) and 0.86 (95% CI 0.76-0.97). These models however did not outperform a previously established four-feature semantic model.

CONCLUSION

Prediction models based on MRI radiomics non-invasively predict tumor response after chemoradiation for rectal cancer and can be used as an additional tool to identify patients eligible for an organ-preserving treatment.

Accurate outcome prediction after neo-adjuvant radio-chemotherapy for rectal cancer based on a TCP-based early regression index

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A TCP-based early regression index (ERI_TCP) was previously introduced.

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ERI_TCP was associated to improved survival after neo-adjuvant therapy for rectal cancer.

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Distant-metastasis-free survival was predicted by ERI_TCP and 5-FU dose.

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The resulting AUC (0.86) was significantly higher than models not including T ERI_TCP.

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ERI_TCP is a promising tool for therapy personalization.

Background and purpose

An early tumor regression index (ERI_TCP) was previously introduced and found to predict pathological response after neo-adjuvant radio-chemotherapy of rectal cancer. ERI_TCP was tested as a potential biomarker in predicting long-term disease-free survival.

Materials and methods

Data of 65 patients treated with an early regression-guided adaptive boosting technique (ART) were available. Overall, loco-regional relapse-free and distant metastasis-free survival (OS, LRFS, DMFS) were considered. Patients received 41.4 Gy in 18 fractions (2.3 Gy/fr), including ART concomitant boost on the residual GTV during the last 6 fractions (3 Gy/fr, D_mean: 45.6 Gy). Chemotherapy included oxaliplatin and 5-fluorouracil (5-FU). T2-weighted MRI taken before (MRI_pre) and at half therapy (MRI_half) were available and GTVs were contoured (V_pre, V_half). The parameter ERI_TCP = −ln[(1 − (V_half/V_pre))^Vpre] was calculated for all patients. Cox regression models were assessed considering several clinical and histological variables. Cox models not including/including ERI_TCP (CONV_model and REGR_model respectively) were assessed and their discriminative power compared.

Results

At a median follow-up of 47 months, OS, LRFS and DMFS were 94%, 95% and 78%. Due to too few events, multivariable analyses focused on DMFS: the resulting CONV_model included pathological complete remission or clinical complete remission followed by surgery refusal (HR: 0.15, p = 0.07) and 5-FU dose >90% (HR: 0.29, p = 0.03) as best predictors, with AUC = 0.75. REGR_model included ERI_TCP (HR: 1.019, p < 0.0001) and 5-FU dose >90% (HR: 0.18, p = 0.005); AUC was 0.86, significantly higher than CONV_model (p = 0.05). Stratifying patients according to the best cut-off value for ERI_TCP and to 5-FU dose (> vs <90%) resulted in 47-month DMFS equal to 100%/69%/0% for patients with two/one/zero positive factors respectively (p = 0.0002). ERI_TCP was also the only variable significantly associated to OS (p = 0.01) and LRFS (p = 0.03).

Conclusion

ERI_TCP predicts long-term DMFS after radio-chemotherapy for rectal cancer: an independent impact of the 5-FU dose was also found. This result represents a first step toward application of ERI_TCP in treatment personalization: additional confirmation on independent cohorts is warranted.

Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes

BACKGROUND

Radiomics-based non-invasive biomarkers are promising to facilitate the translation of therapeutically related molecular subtypes for treatment allocation of patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

We included 113 HNSCC patients from The Cancer Genome Atlas (TCGA-HNSCC) project. Molecular phenotypes analyzed were RNA-defined HPV status, five DNA methylation subtypes, four gene expression subtypes and five somatic gene mutations. A total of 540 quantitative image features were extracted from pre-treatment CT scans. Features were selected and used in a regularized logistic regression model to build binary classifiers for each molecular subtype. Models were evaluated using the average area under the Receiver Operator Characteristic curve (AUC) of a stratified 10-fold cross-validation procedure repeated 10 times. Next, an HPV model was trained with the TCGA-HNSCC, and tested on a Stanford cohort (N = 53).

FINDINGS

Our results show that quantitative image features are capable of distinguishing several molecular phenotypes. We obtained significant predictive performance for RNA-defined HPV+ (AUC = 0.73), DNA methylation subtypes MethylMix HPV+ (AUC = 0.79), non-CIMP-atypical (AUC = 0.77) and Stem-like-Smoking (AUC = 0.71), and mutation of NSD1 (AUC = 0.73). We externally validated the HPV prediction model (AUC = 0.76) on the Stanford cohort. When compared to clinical models, radiomic models were superior to subtypes such as NOTCH1 mutation and DNA methylation subtype non-CIMP-atypical while were inferior for DNA methylation subtype CIMP-atypical and NSD1 mutation.

INTERPRETATION

Our study demonstrates that radiomics can potentially serve as a non-invasive tool to identify treatment-relevant subtypes of HNSCC, opening up the possibility for patient stratification, treatment allocation and inclusion in clinical trials. FUND: Dr. Gevaert reports grants from National Institute of Dental & Craniofacial Research (NIDCR) U01 DE025188, grants from National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (NIBIB), R01 EB020527, grants from National Cancer Institute (NCI), U01 CA217851, during the conduct of the study; Dr. Huang and Dr. Zhu report grants from China Scholarship Council (Grant NO:201606320087), grants from China Medical Board Collaborating Program (Grant NO:15-216), the Cyrus Tang Foundation, and the Zhejiang University Education Foundation during the conduct of the study; Dr. Cintra reports grants from São Paulo State Foundation for Teaching and Research (FAPESP), during the conduct of the study.

Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing

Quantitative imaging features (radiomics) extracted from apparent diffusion coefficient (ADC) maps of rectal cancer patients can provide additional information to support treatment decision. Most available radiomic computational packages allow extraction of hundreds to thousands of features. However, two major factors can influence the reproducibility of radiomic features: interobserver variability, and imaging filtering applied prior to features extraction. In this exploratory study we seek to determine to what extent various commonly-used features are reproducible with regards to the mentioned factors using ADC maps from two different clinics (56 patients). Features derived from intensity distribution histograms are less sensitive to manual tumour delineation differences, noise in ADC images, pixel size resampling and intensity discretization. Shape features appear to be strongly affected by delineation quality. On the whole, textural features appear to be poorly or moderately reproducible with respect to the image pre-processing perturbations we reproduced.

Emerging Functional Imaging Biomarkers of Tumour Responses to Radiotherapy

Tumour responses to radiotherapy are currently primarily assessed by changes in size. Imaging permits non-invasive, whole-body assessment of tumour burden and guides treatment options for most tumours. However, in most tumours, changes in size are slow to manifest and can sometimes be difficult to interpret or misleading, potentially leading to prolonged durations of ineffective treatment and delays in changing therapy. Functional imaging techniques that monitor biological processes have the potential to detect tumour responses to treatment earlier and refine treatment options based on tumour biology rather than solely on size and staging. By considering the biological effects of radiotherapy, this review focusses on emerging functional imaging techniques with the potential to augment morphological imaging and serve as biomarkers of early response to radiotherapy.

Use of magnetic resonance imaging in rectal cancer patients: Society of Abdominal Radiology (SAR) rectal cancer disease-focused panel (DFP) recommendations 2017

PURPOSE

To propose guidelines based on an expert-panel-derived unified approach to the technical performance, interpretation, and reporting of MRI for baseline and post-treatment staging of rectal carcinoma.

METHODS

A consensus-based questionnaire adopted with permission and modified from the European Society of Gastrointestinal and Abdominal Radiologists was sent to a 17-member expert panel from the Rectal Cancer Disease-Focused Panel of the Society of Abdominal Radiology containing 268 question parts. Consensus on an answer was defined as ≥ 70% agreement. Answers not reaching consensus (< 70%) were noted.

RESULTS

Consensus was reached for 87% of items from which recommendations regarding patient preparation, technical performance, pulse sequence acquisition, and criteria for MRI assessment at initial staging and restaging exams and for MRI reporting were constructed.

CONCLUSION

These expert consensus recommendations can be used as guidelines for primary and post-treatment staging of rectal cancer using MRI.

Potentials and challenges of diffusion-weighted magnetic resonance imaging in radiotherapy

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Discussion of DW imaging protocols and imaging setup.

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Discussion of mono- and bi-exponential models for quantitative parameter extraction.

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Review of recent publications investigating potential benefits of using DWI in RT, including detailed synoptic table.

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Detailed discussion of geometric and quantitative accuracy of DW imaging and DW-derived parameters.

Purpose

To review the potential and challenges of integrating diffusion weighted magnetic resonance imaging (DWI) into radiotherapy (RT).

Content

Details related to image acquisition of DWI for RT purposes are discussed, along with the challenges with respect to geometric accuracy and the robustness of quantitative parameter extraction. An overview of diffusion- and perfusion-related parameters derived from mono- and bi-exponential models is provided, and their role as potential RT biomarkers is discussed. Recent studies demonstrating potential of DWI in different tumor sites such as the head and neck, rectum, cervix, prostate, and brain, are reviewed in detail.

Conclusion

DWI has shown promise for RT outcome prediction, response assessment, as well as for tumor delineation and characterization in several cancer types. Geometric and quantification robustness is challenging and has to be addressed adequately. Evaluation in larger clinical trials with well designed imaging protocol and advanced analysis models is needed to develop the optimal strategy for integrating DWI in RT.

'Watch and wait' in rectal cancer: summary of the current evidence

BACKGROUND

The management of rectal cancer has evolved considerably over the last few decades with increasing use of neoadjuvant chemoradiotherapy (nCRT). Complete clinical response (cCR) and even complete pathological response (pCR) have been noted in a proportion of patients who had surgery after nCRT. This raises the concern that we may have been 'over-treating' some of these patients and lead to an increasing interest in 'watch and wait' (W&W) approach for patients who had cCR to avoid the morbidity associated with rectal surgery.

METHODS

A review of the literature in English pertaining to rectal cancer in the context of W&W, organ preservation and active surveillance.

RESULTS

Evidence available to support W&W approach comes from non-randomised controlled trials (RCTs) with no current consensus on patients' selection criteria, lack of viable predictors of both cCR and pCR and lack of universal definitions of cCR and pCR. Also, there is no agreed protocol for disease surveillance.

CONCLUSION

Even though there has been increasing reports on the outcomes of W&W in rectal cancer, the current evidence cannot support its routine use in clinical practice. This approach should be used in clinical trials settings or after thorough counselling with the patient on the outcomes of various treatment options.

VEGF-C is positively associated with lymphangiogenesis and lymphatic metastasis in rectal cancer

Rectal cancer is a common malignancywith a less than 5-year postoperative survival rate. Although it often metastasizes via the lymph and blood, the detailed mechanism of this process remains unclear. This study investigated the relationship between vascular endothelial growth factor-C (VEGF-C) expression and lymphangiogenesis, as well as its relation to lymphatic metastasis of rectal cancer. To address this question, VEGF-C expression in rectal cancer and normal tissue adjacent to tumor was assessed by immunohistochemistry. The lymphatic endothelial cell-specific marker D2-40 was used to label lymphatic endothelial cells and the lymphatic vessel density (LVD) was subsequently quantified. As expected, the expression of VEGF-C in rectal cancer (75%) was significantly higher than in normal adjacent tissue (25%), and this level correlated with differentiation, Dukes stage, and lymph node metastasis, though not with sex or age. The LVD was higher in VEGF-C positive rectal cancer than in VEGF-C negative rectal cancer, and was also higher in lymphatic metastases than in non-lymphatic metastases. These results indicate that expression of VEGF-C may impact the prognosis of rectal cancer via its effect on the formation of new lymphatic vessels. This represents a significant advance in the study of the genesis and development of rectal cancer, and may have value in clinical care.

Development and validation of an MRI-based model to predict response to chemoradiotherapy for rectal cancer

BACKGROUND AND PURPOSE

To safely implement organ preserving treatment strategies for patients with rectal cancer, well-considered selection of patients with favourable response is needed. In this study, we develop and validate an MRI-based response predicting model.

METHODS

A multivariate model using T2-volumetric and DWI parameters before and 6 weeks after chemoradiation (CRT) was developed using a cohort of 85 rectal cancer patients and validated in an external cohort of 55 patients that underwent preoperative CRT.

RESULTS

Twenty-two patients (26%) achieved ypT0-1N0 response in the development cohort versus 13 patients (24%) in the validation cohort. Two T2-volumetric parameters (ΔVolume% and Sphere_post) and two DWI parameters (ADC_avg_post and ADCratio_avg) were retained in a model predicting (near-)complete response (ypT0-1N0). In the development cohort, this model had a good predictive performance (AUC = 0.89; 95% CI 0.80-0.98). Validation of the model in an external cohort resulted in a similar performance (AUC = 0.88 95% CI 0.79-0.98).

CONCLUSION

An MRI-based prediction model of (near-)complete pathological response following CRT in rectal cancer patients, shows a high predictive performance in an external validation cohort. The clinically relevant features in the model make it an interesting tool for implementation of organ-preserving strategies in rectal cancer.