Can clinical factors be used as a selection tool for an organ-preserving strategy in rectal cancer?

Oncological outcomes after a pathological complete response following total neoadjuvant therapy or chemoradiotherapy for high-risk locally advanced rectal cancer in the RAPIDO trial

BACKGROUND

A pathological complete response (pCR) following chemoradiation (CRT) or short-course radiotherapy (scRT) leads to a favourable prognosis in patients with rectal cancer. Total neo-adjuvant therapy (TNT) doubles the pCR rate, but it is unknown whether oncological outcomes remain favourable and whether the same characteristics are associated with pCR as after CRT.

METHODS

Comparison between patients with pCR in the RAPIDO trial in the experimental [EXP] (scRT, chemotherapy, surgery, as TNT) and standard-of-care treatment [STD] (CRT, surgery, postoperative chemotherapy depending on hospital policy) groups. Primary and secondary outcomes were time-to-recurrence (TTR), overall survival (OS) and association between patient, tumour, and treatment characteristics and pCR.

RESULTS

Among patients with a resection within six months after preoperative treatment, 120/423 (28%) [EXP] and 57/398 (14%) [STD] achieved a pCR. Following pCR, 5-year cumulative TTR and OS rates in the EXP and STD arms were 8% vs. 7% (hazard ratio 1.04, 95%CI 0.32-3.38) and 94% vs. 93% (hazard ratio 1.41, 95%CI 0.51-3.92), respectively. Besides the EXP treatment (odds ratio 2.70, 95%CI 1.83-3.97), pre-treatment carcinoembryonic antigen (CEA) <5, pre-treatment tumour size <40 mm and cT2 were associated with pCR. Distance from the anal verge was the only characteristic with a statistically significant difference in association with pCR between the EXP and STD treatment (Pinteraction=0.042). pCR rates did not increase with prolonged treatment time.

CONCLUSIONS

The doubled pCR rate of TNT compared to CRT results in similar oncological outcomes. Characteristics associated with pCR are the EXP treatment, normal CEA, and small tumour size.

Validated Pretreatment Prediction Models for Response to Neoadjuvant Therapy in Patients with Rectal Cancer: A Systematic Review and Critical Appraisal

Pretreatment response prediction is crucial to select those patients with rectal cancer who will benefit from organ preservation strategies following (intensified) neoadjuvant therapy and to avoid unnecessary toxicity in those who will not. The combination of individual predictors in multivariable prediction models might improve predictive accuracy. The aim of this systematic review was to summarize and critically appraise validated pretreatment prediction models (other than radiomics-based models or image-based deep learning models) for response to neoadjuvant therapy in patients with rectal cancer and provide evidence-based recommendations for future research. MEDLINE via Ovid, Embase.com, and Scopus were searched for eligible studies published up to November 2022. A total of 5006 studies were screened and 16 were included for data extraction and risk of bias assessment using Prediction model Risk Of Bias Assessment Tool (PROBAST). All selected models were unique and grouped into five predictor categories: clinical, combined, genetics, metabolites, and pathology. Studies generally included patients with intermediate or advanced tumor stages who were treated with neoadjuvant chemoradiotherapy. Evaluated outcomes were pathological complete response and pathological tumor response. All studies were considered to have a high risk of bias and none of the models were externally validated in an independent study. Discriminative performances, estimated with the area under the curve (AUC), ranged per predictor category from 0.60 to 0.70 (clinical), 0.78 to 0.81 (combined), 0.66 to 0.91 (genetics), 0.54 to 0.80 (metabolites), and 0.71 to 0.91 (pathology). Model calibration outcomes were reported in five studies. Two collagen feature-based models showed the best predictive performance (AUCs 0.83-0.91 and good calibration). In conclusion, some pretreatment models for response prediction in rectal cancer show encouraging predictive potential but, given the high risk of bias in these studies, their value should be evaluated in future, well-designed studies.

Associations between Response to Commonly Used Neo-Adjuvant Schedules in Rectal Cancer and Routinely Collected Clinical and Imaging Parameters

Complete pathological response (pCR) is achieved in 10−20% of rectal cancers when treated with short-course radiotherapy (scRT) or long-course chemoradiotherapy (CRT) and in 28% with total neoadjuvant therapy (scRT/CRT + CTX). pCR is associated with better outcomes and a “watch-and-wait” strategy (W&W). The aim of this study was to identify baseline clinical or imaging factors predicting pCR. All patients with preoperative treatment and delays to surgery in Uppsala-Dalarna (n = 359) and Stockholm (n = 635) were included. Comparison of pCR versus non-pCR was performed with binary logistic regression models. Receiver operating characteristics (ROC) models for predicting pCR were built using factors with p < 0.10 in multivariate analyses. A pCR was achieved in 12% of the 994 patients (scRT 8% [33/435], CRT 13% [48/358], scRT/CRT + CTX 21% [43/201]). In univariate and multivariate analyses, choice of CRT (OR 2.62; 95%CI 1.34−5.14, scRT reference) or scRT/CRT + CTX (4.70; 2.23−9.93), cT1−2 (3.37; 1.30−8.78; cT4 reference), tumour length ≤ 3.5 cm (2.27; 1.24−4.18), and CEA ≤ 5 µg/L (1.73; 1.04−2.90) demonstrated significant associations with achievement of pCR. Age < 70 years, time from radiotherapy to surgery > 11 weeks, leucocytes ≤ 109/L, and thrombocytes ≤ 4009/L were significant only in univariate analyses. The associations were not fundamentally different between treatments. A model including T-stage, tumour length, CEA, and leucocytes (with scores of 0, 0.5, or 1 for each factor, maximum 4 points) showed an area under the curve (AUC) of 0.66 (95%CI 0.60−0.71) for all patients, and 0.65−0.73 for the three treatments separately. The choice of neoadjuvant treatment in combination with low CEA, short tumour length, low cT-stage, and normal leucocytes provide support in predicting pCR and, thus, could offer guidance for selecting patients for organ preservation.

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.

Predicting pathological response to chemoradiotherapy for rectal cancer: a systematic review

Introduction: Pathological complete response (pCR) rates of approximately 20% following neoadjuvant long-course chemoradiotherapy for rectal cancer have given rise to non-operative or watch-and-wait (W&W) management. To improve outcomes there has been significant research into predictors of response. The goal is to optimize selection for W&W, avoid chemoradiotherapy in those who won't benefit and improve treatment to maximize the clinical complete response (cCR) rate and the number of patients who can be considered for W&W.Areas covered: A systematic review of articles published 2008-2018 and indexed in PubMed, Embase or Medline was performed to identify predictors of pathological response (including pCR and recognized tumor regression grades) to fluoropyrimidine-based chemoradiotherapy in patients who underwent total mesorectal excision for rectal cancer. Evidence for clinical, biomarker and radiological predictors is discussed as well as potential future directions.Expert opinion: Our current ability to predict the response to chemoradiotherapy for rectal cancer is very limited. cCR of 40% has been achieved with total neoadjuvant therapy. If neoadjuvant treatment for rectal cancer continues to improve it is possible that the treatment for rectal cancer may eventually parallel that of anal squamous cell carcinoma, with surgery reserved for the minority of patients who don't respond to chemoradiotherapy.

Clinical predictors of response to chemoradiotherapy for rectal cancer as an aid to organ preservation

AIM

Clinical predictors of pathological response to chemoradiotherapy for rectal cancer can influence patient management including selection for organ preservation. This study aimed to identify clinical predictors at a tertiary referral hospital.

METHODS

A retrospective review of clinical records was undertaken after identifying all patients with stage 1-3 rectal cancer treated with long course chemoradiotherapy and total mesorectal excision from 2013 to 2018. Clinicopathological factors were recorded and multivariate analysis performed to identify predictors of pathological complete response (pCR) and good response (AJCC TRG 0-1).

RESULTS

A total of 470 patients with rectal cancer were identified of which 164 met the inclusion criteria for the study. The pCR rate was 14.6% and good response (TRG 0-1) rate 43.7%. On univariate analysis, lower T stage, older age, node negative status, anterior tumour position and shorter tumour length on magnetic resonance imaging (MRI) were associated with good response (TRG 0-1). On univariate analysis cN stage, carcinoembryonic antigen <5 and shorter tumour length on MRI were associated with pCR. On binary logistic regression shorter length on MRI and lower clinical nodal stage were predictive of pCR and lower body mass index, anterior tumour position and higher haemoglobin were predictive of good response (TRG 0-1).

CONCLUSION

Anterior tumour position is newly identified as an independent predictor of good response (TRG 0-1) to nCRT for rectal cancer and this should be explored in future studies. Higher haemoglobin and lower body mass index were also independent predictors of good response (TRG 0-1) and optimisation of these factors should be considered when using neoadjuvant chemoradiotherapy for rectal cancer.

A Comprehensive Evaluation of Associations Between Routinely Collected Staging Information and The Response to (Chemo)Radiotherapy in Rectal Cancer

Simple Summary

Rectal cancer patients are often treated with radiotherapy, either alone or combined with chemotherapy, prior to surgery to enable radical surgery on a non-resectable tumor or to lower the recurrence risk. For some patients, the tumor disappears completely after preoperative treatment, while others experience little or no benefit. Accurate prediction of therapy response before treatment is of great importance for a personalized treatment approach and intentional organ preservation. We performed a comprehensive evaluation of the predictive capacity of all routinely collected staging information at diagnosis in a population-based, completely staged patient material of 383 patients representing a real-life clinical situation. Size or stage of the rectal tumor were independent predictors of excellent response irrespective of preoperative treatment, with small/early-stage tumors being significantly more likely to reach a complete response. Levels of the tumor marker carcinoembryonic antigen (CEA) above upper normal limit halved the chance of response.

Abstract

Radiotherapy (RT) or chemoradiotherapy (CRT) are frequently used in rectal cancer, sometimes resulting in complete tumor remission (CR). The predictive capacity of all clinical factors, laboratory values and magnetic resonance imaging parameters performed in routine staging was evaluated to understand what determines an excellent response to RT/CRT. A population-based cohort of 383 patients treated with short-course RT (5 × 5 Gy in one week, scRT), CRT, or scRT with chemotherapy (scRT+CT) and having either had a delay to surgery or been entered into a watch-and-wait program were included. Complete staging according to guidelines was performed and associations between investigated variables and CR rates were analyzed in univariate and multivariate analyses. In total, 17% achieved pathological or clinical CR, more often after scRT+CT and CRT than after scRT (27%, 18% and 8%, respectively, p < 0.001). Factors independently associated with CR included clinical tumor stage, small tumor size (<3 cm), tumor level, and low CEA-value (<3.8 μg/L). Size or stage of the rectal tumor were associated with excellent response in all therapy groups, with small or early stage tumors being significantly more likely to reach CR (p = 0.01 (scRT), p = 0.01 (CRT) and p = 0.02 (scRT+CT). Elevated level of carcinoembryonic antigen (CEA) halved the chance of response. Extramural vascular invasion (EMVI) and mucinous character may indicate less response to RT alone.

Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis

Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization. Our method is built upon matrix tri-factorization enhanced by adaptive sparsity regularization for simultaneous feature dimensionality reduction and denoising. Particularly, latent grouping information of patients with distinct radiomic features is learned and utilized as supervision information to guide the feature dimensionality reduction, and noise in radiomic features is adaptively isolated in a Bayesian framework under a general assumption of Laplacian distributions of transform-domain coefficients. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and evaluation results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.

COLLABORATIVE CLUSTERING OF SUBJECTS AND RADIOMIC FEATURES FOR PREDICTING CLINICAL OUTCOMES OF RECTAL CANCER PATIENTS

Most machine learning approaches in radiomics studies ignore the underlying difference of radiomic features computed from heterogeneous groups of patients, and intrinsic correlations of the features are not fully exploited yet. In order to better predict clinical outcomes of cancer patients, we adopt an unsupervised machine learning method to simultaneously stratify cancer patients into distinct risk groups based on their radiomic features and learn low-dimensional representations of the radiomic features for robust prediction of their clinical outcomes. Based on nonnegative matrix tri-factorization techniques, the proposed method applies collaborative clustering to radiomic features of cancer patients to obtain clusters of both the patients and their radiomic features so that patients with distinct imaging patterns are stratified into different risk groups and highly correlated radiomic features are grouped in the same radiomic feature clusters. Experiments on a FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method facilitates better stratification of patients with distinct survival patterns and learning of more effective low-dimensional feature representations that ultimately leads to accurate prediction of patient survival, outperforming conventional methods under comparison.

DEEP CONVOLUTIONAL NEURAL NETWORKS FOR IMAGING DATA BASED SURVIVAL ANALYSIS OF RECTAL CANCER

Recent radiomic studies have witnessed promising performance of deep learning techniques in learning radiomic features and fusing multimodal imaging data. Most existing deep learning based radiomic studies build predictive models in a setting of pattern classification, not appropriate for survival analysis studies where some data samples have incomplete observations. To improve existing survival analysis techniques whose performance is hinged on imaging features, we propose a deep learning method to build survival regression models by optimizing imaging features with deep convolutional neural networks (CNNs) in a proportional hazards model. To make the CNNs applicable to tumors with varied sizes, a spatial pyramid pooling strategy is adopted. Our method has been validated based on a simulated imaging dataset and a FDG-PET/CT dataset of rectal cancer patients treated for locally advanced rectal cancer. Compared with survival prediction models built upon hand-crafted radiomic features using Cox proportional hazards model and random survival forests, our method achieved competitive prediction performance.

Nomograms for predicting pathological response to neoadjuvant treatments in patients with rectal cancer

BACKGROUND

In recent decades, neoadjuvant therapy (NT) has been the standardized treatment for locally advanced rectal cancer (LARC). Approximately 8%-35% of patients with LARC who received NT were reported to have achieved a complete pathological response (pCR). If the pathological response (PR) can be accurately predicted, these patients may not need surgery. In addition, no response after NT implies that the tumor is destructive, resistant to both chemotherapy and radiotherapy, and prone to having a high metastatic potential. Therefore, developing accurate models to predict PR has great clinical significance and can help achieve individualized treatment in LARC patients.

AIM

To establish nomograms for predicting PR to different NT regimens based on pretreatment parameters for patients with LARC.

METHODS

Rectal cancer patients were identified from the database of The Sixth Affiliated Hospital, Sun Yat-sen University from January 2012 to December 2016. Logistic regression and nomograms were developed to predict the probability of pCR and good downstaging to ypT0-2N0M0 (ypTNM 0-I), respectively, based on pretreatment parameters for all LARC patients. Nomograms were also developed for three NT regimens (capecitabine/deGramont-RT, mFOLFOX6, and mFOLFOX6-RT) to predict pCR probability.

RESULTS

Four hundred and three patients were included in this study; 72 (17.9%) had pCR at the final pathology report, and 177 (43.9%) achieved good downstaging to ypT0-2N0M0 (ypTNM 0-I). The nomogram for predicting pCR probability showed that NT regimens, tumor differentiation, mesorectal fascia (MRF) status, and tumor length significantly influenced pCR probability. When predicting the probability of good downstaging, tumor differentiation, MRF status, and clinical T stage were the significant factors. Nomograms were developed based on NT regimens. For the capecitabine/de Gramont-RT group, the multivariate analysis showed that the neutrophil-lymphocyte ratio (NLR) was the only significant factor, thus we could not develop a nomogram for this regimen. For the mFOLFOX6-RT group, the analysis showed that the significant factors were tumor length and MRF status; and for the mFOLFOX6 group, the significant factors were tumor length and tumor differentiation.

CONCLUSION

We established accurate nomograms for predicting the PR to preoperative NT regimens based on pretreatment parameters for LARC patients.

Morphologic predictors of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Purpose

To evaluate the value of morphological parameters that can be obtained conveniently by MRI for predicting pathologically complete response (pCR) in patients with rectal cancer.

Materials and Methods

A cohort of 101 patients was examined using MRI before and after Neoadjuvant chemoradiotherapy (nCRT). Morphological parameters including maximum tumor area (MTA), maximum tumor length (MTL) and maximum tumor thickness (MTT), as well as cylindrical approximated tumor volume (CATV), distance to anal verge (DTA), and the reduction rates were evaluated by two experienced readers independently.

Results

Post-nCRT MTA and MTL, reduction rates and pre-nCRT DTA were proved to be significantly different between pCR and non-pCR with the AUCs of 0.672-0.853. The sensitivity and specificity for assessing pCR were 61.1-89.9% and 59.0-80.7% respectively. No significant correlation between pre-nCRT size measurements and pCR was obtained.

Conclusion

The convenient morphological measurements may be useful for predicting pCR with moderate sensitivity and specificity. Combining these predictors with the aim of building diagnostic model should be explored.

New insights into colorectal cancer screening and early detection tests

Colorectal cancer (CRC) is a common cancer in both men and women worldwide. Creating a diagnostic panel is necessary for early diagnosis which could lead to a better long-term survival in cancer patients. Colonoscopy every 10 years, starting at age 50, is the preferred CRC screening test. Many studies have been worked on potential diagnostic biomarkers of CRC. In this article, we described the recent evolutions in the development of CRC noninvasive screening assays. Recently, a multifunctional fecal DNA test has been available commercially in the USA. A few other US FDA-approved tests like Epi proColon® (Epigenomics AG, Berlin, Germany) are also available now. Although a new marker class for fecal occult blood test, a novel biomarker based on fecal bacteria in CRC patients and circulating tumor cells are under investigation, there is still a strong need to do more research for CRC screening strategies.

On a prolonged interval between rectal cancer (chemo)radiotherapy and surgery

Preoperative radiotherapy (RT) or chemoradiotherapy (CRT) is often required before rectal cancer surgery to obtain low local recurrence rates or, in locally advanced tumours, to radically remove the tumour. RT/CRT in tumours responding completely can allow an organ-preserving strategy. The time from the end of the RT/CRT to surgery or to the decision not to operate has been prolonged during recent years. After a brief review of the literature, the relevance of the time interval to surgery is discussed depending upon the indication for RT/CRT. In intermediate rectal cancers, where the aim is to decrease local recurrence rates without any need for down-sizing/-staging, short-course RT with immediate surgery is appropriate. In elderly patients at risk for surgical complications, surgery could be delayed 5-8 weeks. If CRT is used, surgery should be performed when the acute radiation reaction has subsided or after 5-6 weeks. In locally advanced tumours, where CRT is indicated, the optimal delay is 6-8 weeks. In patients not tolerating CRT, short-course RT with a 6-8-week delay is an alternative. If organ preservation is a goal, a first evaluation should preferably be carried out after about 6 weeks, with planned surgery for week 8 if the response is inadequate. In case the response is good, a new evaluation should be carried out after about 12 weeks, with a decision to start a 'watch-and-wait' programme or operate. Chemotherapy in the waiting period is an interesting option, and has been the subject of recent trials with promising results.