Could the conservative approach be considered safe in the treatment of locally advanced rectal cancer in case of a clinical near-complete or complete response? A retrospective analysis

MOREOVER: multiomics MR-guided radiotherapy optimization in locally advanced rectal cancer

BACKGROUND

Complete response prediction in locally advanced rectal cancer (LARC) patients is generally focused on the radiomics analysis of staging MRI. Until now, omics information extracted from gut microbiota and circulating tumor DNA (ctDNA) have not been integrated in composite biomarkers-based models, thereby omitting valuable information from the decision-making process. In this study, we aim to integrate radiomics with gut microbiota and ctDNA-based genomics tracking during neoadjuvant chemoradiotherapy (nCRT).

METHODS

The main hypothesis of the MOREOVER study is that the incorporation of composite biomarkers with radiomics-based models used in the THUNDER-2 trial will improve the pathological complete response (pCR) predictive power of such models, paving the way for more accurate and comprehensive personalized treatment approaches. This is due to the inclusion of actionable omics variables that may disclose previously unknown correlations with radiomics. Aims of this study are: - to generate longitudinal microbiome data linked to disease resistance to nCRT and postulate future therapeutic strategies in terms of both type of treatment and timing, such as fecal microbiota transplant in non-responding patients. - to describe the genomics pattern and ctDNA data evolution throughout the nCRT treatment in order to support the prediction outcome and identify new risk-category stratification agents. - to mine and combine collected data through integrated multi-omics approaches (radiomics, metagenomics, metabolomics, metatranscriptomics, human genomics, ctDNA) in order to increase the performance of the radiomics-based response predictive model for LARC patients undergoing nCRT on MR-Linac.

EXPERIMENTAL DESIGN

The objective of the MOREOVER project is to enrich the phase II THUNDER-2 trial (NCT04815694) with gut microbiota and ctDNA omics information, by exploring the possibility to enhance predictive performance of the developed model. Longitudinal ctDNA genomics, microbiome and genomics data will be analyzed on 7 timepoints: prior to nCRT, during nCRT on a weekly basis and prior to surgery. Specific modelling will be performed for data harvested, according to the TRIPOD statements.

DISCUSSION

We expect to find differences in fecal microbiome, ctDNA and radiomics profiles between the two groups of patients (pCR and not pCR). In addition, we expect to find a variability in the stability of the considered omics features over time. The identified profiles will be inserted into dedicated modelling solutions to set up a multiomics decision support system able to achieve personalized treatments.

Neoadjuvant treatment of colorectal cancer: comprehensive review

BACKGROUND

Neoadjuvant therapy has an established role in the treatment of patients with colorectal cancer. However, its role continues to evolve due to both advances in the available treatment modalities, and refinements in the indications for neoadjuvant treatment and subsequent surgery.

METHODS

A narrative review of the most recent relevant literature was conducted.

RESULTS

Short-course radiotherapy and long-course chemoradiotherapy have an established role in improving local but not systemic disease control in patients with rectal cancer. Total neoadjuvant therapy offers advantages over short-course radiotherapy and long-course chemoradiotherapy, not only in terms of increased local response but also in reducing the risk of systemic relapses. Non-operative management is increasingly preferred to surgery in patients with rectal cancer and clinical complete responses but is still associated with some negative impacts on functional outcomes. Neoadjuvant chemotherapy may be of some benefit in patients with locally advanced colon cancer with proficient mismatch repair, although patient selection is a major challenge. Neoadjuvant immunotherapy in patients with deficient mismatch repair cancers in the colon or rectum is altering the treatment paradigm for these patients.

CONCLUSION

Neoadjuvant treatments for patients with colon or rectal cancers continue to evolve, increasing the complexity of decision-making for patients and clinicians alike. This review describes the current guidance and most recent developments.

Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study

PURPOSE

The accurate prediction of treatment response in locally advanced rectal cancer (LARC) patients undergoing MRI-guided radiotherapy (MRIgRT) is essential for optimising treatment strategies. This multi-institutional study aimed to investigate the potential of radiomics in enhancing the predictive power of a known radiobiological parameter (Early Regression Index, ERITCP) to evaluate treatment response in LARC patients treated with MRIgRT.

METHODS

Patients from three international sites were included and divided into training and validation sets. 0.35 T T2*/T1-weighted MR images were acquired during simulation and at each treatment fraction. The biologically effective dose (BED) conversion was used to account for different radiotherapy schemes: gross tumour volume was delineated on the MR images corresponding to specific BED levels and radiomic features were then extracted. Multiple logistic regression models were calculated, combining ERITCP with other radiomic features. The predictive performance of the different models was evaluated on both training and validation sets by calculating the receiver operating characteristic (ROC) curves.

RESULTS

A total of 91 patients was enrolled: 58 were used as training, 33 as validation. Overall, pCR was observed in 25 cases. The model showing the highest performance was obtained combining ERITCP at BED = 26 Gy with a radiomic feature (10th percentile of grey level histogram, 10GLH) calculated at BED = 40 Gy. The area under ROC curve (AUC) of this combined model was 0.98 for training set and 0.92 for validation set, significantly higher (p = 0.04) than the AUC value obtained using ERITCP alone (0.94 in training and 0.89 in validation set).

CONCLUSION

The integration of the radiomic analysis with ERITCP improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35 T MRIgRT treatments of LARC patients.

Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer

BACKGROUND

For locally advanced rectal cancer (LARC), accurate response evaluation is necessary to select complete responders after neoadjuvant therapy (NAT) for a watch-and-wait (W&W) strategy. Algorithms based on deep learning have shown great value in medical image analyses. Here we used deep learning algorithms of endoscopic images for the assessment of NAT response in LARC.

METHOD

214 LARC patients were retrospectively included in the study. After NAT, these patients underwent total mesorectal excision (TME) surgery. Among them, 51 (23.8%) of the patients achieved a pathological complete response (pCR). 160 patients from Shanghai Changzheng Hospital were regarded as primary dataset, and the other 54 patients from Zhejiang Cancer Hospital were regarded as validation dataset. ResNet-18 and DenseNet-121 were applied to train the models based on endoscopic images after NAT. Deep learning models were valid in the validation dataset and compared to manual method.

RESULTS

The performances were comparable in AUC between deep learning models and manual method. For mean metrics, sensitivity (0.750 vs. 0.417) and AUC (0.716 vs. 0.601) in ResNet-18 deep learning model were higher than those in the manual method. The deep learning models were able to identify the endoscopic features associated with NAT response by the heatmaps. A diagnostic flow diagram which integrated the deep learning model to assist the clinicians in making decisions for W&W strategy was constructed.

CONCLUSIONS

We created deep learning models using endoscopic features for assessment of NAT in LARC. The deep learning models achieved modest accuracies and performed comparably to manual method.

A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients

Purpose

Distant metastasis is the main cause of treatment failure in locally advanced rectal cancer (LARC) patients, despite the recent improvement in treatment strategies. This study aims to evaluate the “delta radiomics” approach in patients undergoing neoadjuvant chemoradiotherapy (nCRT) treated with 0.35-T magnetic resonance-guided radiotherapy (MRgRT), developing a logistic regression model able to predict 2-year disease-free-survival (2yDFS).

Methods

Patients affected by LARC were enrolled in this multi-institutional study. A predictive model of 2yDFS was developed taking into account both clinical and radiomics variables. Gross tumour volume (GTV) was delineated on the magnetic resonance (MR) images acquired during MRgRT, and 1,067 radiomic features (RF) were extracted using the MODDICOM platform. The performance of RF in predicting 2yDFS was investigated in terms of the Wilcoxon–Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC).

Results

48 patients have been retrospectively enrolled, with 8 patients (16.7%) developing distant metastases at the 2-year follow-up. A total of 1,099 variables (1,067 RF and 32 clinical variables) were evaluated in two different models: radiomics and radiomics/clinical. The best-performing 2yDFS predictive model was a delta radiomics one, based on the variation in terms of area/surface ratio between biologically effective doses (BED) at 54 Gy and simulation (AUC of 0.92).

Conclusions

The results of this study suggest a promising role of delta radiomics analysis on 0.35-T MR images in predicting 2yDFS for LARC patients. Further analyses including larger cohorts of patients and an external validation are needed to confirm these preliminary results.

BRIDGE -1 TRIAL: BReak Interval Delayed surgery for Gastrointestinal Extraperitoneal rectal cancer, a multicentric phase III randomized trial

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The lengthening of the surgical interval has a positive impact on pathological complete response (pCR) rate.

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Lengthening the surgical interval from the end of preoperative chemoradiation is not detrimental to survival outcomes in locally advanced rectal cancer.

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Prospective validation of the impact of surgical interval lengthening on complete response and survival outcomes in a randomized trial.

Design

Methods

Conclusions

Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development

PURPOSE

Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC).

RESULTS

The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (p = 0.6) between yMTB and CM.

CONCLUSION

Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.