Clinical predictors of pathological good response in locally advanced rectal cancer

Can Pretreatment MRI and Planning CT Radiomics Improve Prediction of Complete Pathological Response in Locally Advanced Rectal Cancer Following Neoadjuvant Treatment?

OBJECTIVE(S)

The treatment response to neoadjuvant chemoradiation (nCRT) differs largely in individuals treated for rectal cancer. In this study, we investigated the role of radiomics to predict the pathological response in locally advanced rectal cancers at different treatment time points: (1) before the start of any treatment using baseline T2-weighted MRI (T2W-MR) and (2) at the start of radiation treatment using planning CT.

METHODS

Patients on nCRT followed by surgery between June 2017 to December 2019 were included in the study. Histopathological tumour response grading (TRG) was used for classification, and gross tumour volume was defined by the radiation oncologists. Following resampling, 100 and 103 pyradiomic features were extracted from T2W-MR and planning CT images, respectively. Synthetic minority oversampling technique (SMOTE) was used to address class imbalance. Four machine learning classifiers built clinical, radiomic, and merged models. Model performances were evaluated on a held-out test dataset following 3-fold cross-validation using area under the receiver operator characteristic curves (AUC) with bootstrap 95% confidence intervals.

RESULTS

One hundred and fifty patients were included; 58/150 with TRG 1 were classified as complete responders, and rest were incomplete responders (IR). Clinical models performed better (AUC = 0.68) compared to radiomics models (AUC = 0.62). Overall, the clinical + T2W-MR model showed best performance (AUC = 0.72) in predicting the pathological response prior to therapy. Clinical + Planning CT-merged models could only achieve the highest AUC of 0.66.

CONCLUSION

Merging clinical and baseline T2W-MR radiomics enhances predicting pathological response in rectal cancer. Validation in larger cohorts is warranted, especially for watch and wait strategies.

Predictive Value of Tumor-Infiltrating Lymphocytes and Ki-67 for Pathological Response to Total Neoadjuvant Therapy in Rectal Cancer

PURPOSE

Patients with locally advanced rectal cancer (LARC) who underwent total neoadjuvant therapy (TNT) showed an increase in the percentage of complete pathological response (pCR). The purpose of this study was to determine the correlation between Ki-67, tumor-infiltrating lymphocytes (TIL), and TNT in LARC patients.

METHOD

In total, one hundred fifty-nine patients with LARC were included in this prospective study. The international working group was used to categorize the TIL into three groups based on the percentage and density of staining: group 0 (0-10%), group 1 (11-59%), and group 2 (≥ 60%). Ki-67 expression was classified as low (≤ 50%) or high (> 50%).

RESULT

Most patients had tumor grade III (74.2%) and T2-T3 (78.6%). Lymph node involvement (48.7%) and tumor size ≥ 3 cm were detected in approximately half of the patients. Forty-four percent of patients had a high Ki-67 index; 15.7% of patients belonged to group 1, and 21.4% belonged to group 2. pCR was detected in 18.2% of the patients. TIL and Ki-67 levels were significantly correlated with pCR (p = 0.001 and 0.003 for multivariate analysis and 0.001 and 0.03 for univariate analysis, respectively).

CONCLUSION

There was a statistically significant correlation between Ki-67, TIL, and pCR following TNT protocol, which may help maximize the therapeutic outcome.

HOXA13 serves as a biomarker to predict neoadjuvant therapy efficacy in advanced colorectal cancer patients

Neoadjuvant therapy (NAT) for advanced colorectal cancer (ACRC) is a kind of well-evidenced therapy, yet a portion of ACRC patients have poor therapeutic response. To date, no suitable biomarker used for assessing NAT efficacy has been reported. Here, we collect 72 colonoscopy biopsy tissue specimens from ACRC patients before undergoing NAT and investigate the relationship between HOXA13 expression and NAT efficacy. The results show that HOXA13 expression in pretreated tumor specimens is negatively associated with tumor regression ( P<0.001) and progression-free survival ( P<0.05) in ACRC patients who underwent NAT. Silencing of HOXA13 or its regulator HOTTIP significantly enhances the chemosensitivity of colorectal cancer (CRC) cells, leading to an increase in cell apoptosis and the DNA damage response (DDR) to chemotherapeutic drug treatment. In contrast, HOXA13 overexpression causes a significant increase in chemoresistance in CRC cells. In summary, we find that the HOTTIP/HOXA13 axis is involved in regulating chemotherapeutic sensitivity in CRC cells by modulating the DDR and that HOXA13 serves as a promising marker for NAT efficacy prediction in ACRC patients.

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.

CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer

BACKGROUND AND PURPOSE

Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT.

MATERIALS AND METHODS

Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application.

RESULTS

This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance.

CONCLUSION

Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.

Predicting response to neoadjuvant chemoradiotherapy in rectal cancer: from biomarkers to tumor models

Colorectal cancer (CRC) is a major contributor to cancer-associated morbidity worldwide and over one-third of CRC is located in the rectum. Neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection is commonly applied to treat locally advanced rectal cancer (LARC). In this review, we summarize current and novel concepts of neoadjuvant therapy for LARC such as total neoadjuvant therapy and describe how these developments impact treatment response. Moreover, as response to nCRT is highly divergent in rectal cancers, we discuss the role of potential predictive biomarkers. We review recent advances in biomarker discovery, from a clinical as well as a histopathological and molecular perspective. Furthermore, the role of emerging predictive biomarkers derived from the tumor environment such as immune cell composition and gut microbiome is presented. Finally, we describe how different tumor models such as patient-derived cancer organoids are used to identify novel predictive biomarkers for chemoradiotherapy (CRT) in rectal cancer.

Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics

BACKGROUND

Histopathologic evaluation after surgery is the gold standard to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). However, it cannot be used to guide organ-preserving strategies due to poor timeliness.

PURPOSE

To develop and validate a multiscale model incorporating radiomics and pathomics features for predicting pathological good response (pGR) of down-staging to stage ypT0-1N0 after nCRT.

STUDY TYPE

Retrospective.

POPULATION

A total of 153 patients (median age, 55 years; 109 men; 107 training group; 46 validation group) with clinicopathologically confirmed LARC.

FIELD STRENGTH/SEQUENCE

A 3.0-T; fast spin echo T₂ -weighted and single-shot EPI diffusion-weighted images.

ASSESSMENT

The differences in clinicoradiological variables between pGR and non-pGR groups were assessed. Pretreatment and posttreatment radiomics signatures, and pathomics signature were constructed. A multiscale pGR prediction model was established. The predictive performance of the model was evaluated and compared to that of the clinicoradiological model.

STATISTICAL TESTS

The χ² test, Fisher's exact test, t-test, the minimum redundancy maximum relevance algorithm, the least absolute shrinkage and selection operator logistic regression algorithm, regression analysis, receiver operating characteristic curve (ROC) analysis, Delong method. P < 0.05 indicated a significant difference.

RESULTS

Pretreatment radiomics signature (odds ratio [OR] = 2.53; 95% CI: 1.58-4.66), posttreatment radiomics signature (OR = 9.59; 95% CI: 3.04-41.46), and pathomics signature (OR = 3.14; 95% CI: 1.40-8.31) were independent factors for predicting pGR. The multiscale model presented good predictive performance with areas under the curve (AUC) of 0.93 (95% CI: 0.88-0.98) and 0.90 (95% CI: 0.78-1.00) in the training and validation groups, those were significantly higher than that of the clinicoradiological model with AUCs of 0.69 (95% CI: 0.55-0.82) and 0.68 (95% CI: 0.46-0.91) in both groups.

DATA CONCLUSION

A model incorporating radiomics and pathomics features effectively predicted pGR after nCRT in patients with LARC.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 4.