Response In Rectal Cancer Patients Research Articles

Lymphocyte-to-monocyte, platelet-to-albumin and platelet-to-lymphocyte ratios as prognostic biomarkers for neoadjuvant treatment response in rectal cancer patients

IntroductionSystemic inflammatory response (SIR) indicators are an emerging category of serum biomarkers with significant potential as prognostic and predictive factors in various types of cancers The primary focus of our study was to determine the prognostic value of the lymphocyte-to-monocyte ratio (LMR), platelet-to-albumin ratio (PLR) and platelet-to-albumin ratio (PAR) in evaluating the response to neoadjuvant treatment for patients with rectal cancer. Materials and methodsWe included 99 consecutive patients with rectal cancer which were admitted for surgery in our institution after completing a standard neoadjuvant radio-chemotherapy regimen. Several hematologic parameters, including LMR, PAR and PLR, were calculated by collecting and analyzing blood samples preoperatively. Cases were divided into groups using ROC curve analysis to determine optimal cutoff values for each of the investigated parameters. Treatment response was assessed through histopathological analysis of the resected specimens. ResultsPLR values over 215.2 were correlated with the presence of lymph node metastasis. A similar correlation was observed between PAR values over 41.89 and lymph node positivity. A significant correlation was observed between the presence of tumor budding on histopathological analysis and high-PAR values. A statistically significant correlation between a high PLR and a good response to neoadjuvant treatment was determined. ConclusionsHigh PLR values may be associated with a more favorable treatment response to neoadjuvant radio-chemotherapy. A high PAR may be associated with unfavorable histopathological characteristics. Further studies on these readily available biomarkers are required in order to validate their clinical utility.

Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients.

The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuvant therapy and potential organ preservation. Of particular interest are predictors of pathological complete response (pCR) that can guide personalized treatment. There are currently no clinical biomarkers which can accurately predict neoadjuvant therapy (NAT) response but body composition (BC) measures present as an emerging contender. The primary aim of the study was to determine if artificial intelligence (AI) derived body composition variables can predict pCR in patients with LARC. LARC patients who underwent NAT followed by surgery from 2012 to 2023 were identified from the Australian Comprehensive Cancer Outcomes and Research Database registry (ACCORD). A validated in-house pre-trained 3D AI model was used to measure body composition via computed tomography images of the entire Lumbar-3 vertebral level to produce a volumetric measurement of visceral fat (VF), subcutaneous fat (SCF) and skeletal muscle (SM). Multivariate analysis between patient body composition and histological outcomes was performed. Of 214 LARC patients treated with NAT, 22.4% of patients achieved pCR. SM volume (P = 0.015) and age (P = 0.03) were positively associated with pCR in both male and female patients. SCF volume was associated with decreased likelihood of pCR (P = 0.059). This is the first study in the literature utilizing AI-measured 3D Body composition in LARC patients to assess their impact on pathological response. SM volume and age were positive predictors of pCR disease in both male and female patients following NAT for LARC. Future studies investigating the impact of body composition on clinical outcomes and patients on other neoadjuvant regimens such as TNT are potential avenues for further research.

Prognostic Significance of Neutrophil-to-Lymphocyte Ratio in Predicting Complete Pathological Response in Rectal Cancer Patients Receiving Neoadjuvant Chemoradiotherapy.

BACKGROUND Pathologic response after neoadjuvant therapy has been shown to improve outcomes in rectal cancer. Inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), have been studied to predict pathologic response and survival. This study aimed to evaluate the association between NLR and pathological response as well as outcome in patients with rectal cancer who underwent neoadjuvant chemoradiotherapy (nCRT). MATERIAL AND METHODS We retrospectively analyzed 187 patients with rectal cancer treated with nCRT followed by surgery between 2016 and 2020. The NLR was calculated using archival complete blood count records. Postoperative pathology reports were recorded. The NLR cut-off was determined by receiver operating characteristic curve. Kaplan-Meier survival curves and univariate and multivariate Cox regression analyses were used to analyze the relationship between NLR and clinicopathologic data to predict survival and prognosis. RESULTS An NLR >3.63 at diagnosis was the optimal cut-off value for predicting progression. Near-complete response rates were higher in patients with NLR <3.63 (38%) than in those with NLR >3.63 (18%) (P=0.035). The NLR <3.63 group had a significantly higher 5-year progression-free survival rate compared to the NLR >3.63 group (63.6% vs 40.1%, respectively; P=0.007). The NLR <3.63 group also had a higher 5-year overall survival (OS) rate than the NLR >3.63 group (72.3% vs 63.1%, respectively), but the difference was not statistically significant (P=0.077). CONCLUSIONS Our study showed a higher near-complete response rate in rectal cancer patients with NLR <3.63 receiving nCRT. This finding supports that a low preoperative NLR is a good prognostic factor in indicating pathological response.

Early regression index (ERI) on MR images as response predictor in esophageal cancer treated with neoadjuvant chemo-radiotherapy: Interim analysis of the prospective ESCAPE trial

PurposeThe early regression index (ERI) predicts treatment response in rectal cancer patients. Aim of current study was to prospectively assess tumor response to neoadjuvant chemo-radiotherapy (nCRT) of locally advanced esophageal cancer using ERI, based on MRI. Material and methodsFrom January 2020 to May 2023, 30 patients with esophageal cancer were enrolled in a prospective study (ESCAPE). PET-MRI was performed: i) before nCRT (tpre); ii) at mid-radiotherapy, tmid; iii) after nCRT, 2–6 weeks before surgery (tpost); nCRT delivered 41.4 Gy/23fr with concurrent carboplatin and paclitaxel. For patients that skipped surgery, complete clinical response (cCR) was assessed if patients showed no local relapse after 18 months; patients with pathological complete response (pCR) or with cCR were considered as complete responders (pCR + cCR). GTV volumes were delineated by two observers (Vpre, Vmid, Vpost) on T2w MRI: ERI and other volume regression parameters at tmid and tpost were tested as predictors of pCR + cCR. ResultsComplete data of 25 patients were available at the time of the analysis: 3/25 with complete response at imaging refused surgery and 2/3 were cCR; in total, 10/25 patients showed pCR + cCR (pCR = 8/22). Both ERImid and ERIpost classified pCR + cCR patients, with ERImid showing better performance (AUC:0.78, p = 0.014): A two-variable logistic model combining ERImid and Vpre improved performances (AUC:0.93, p < 0.0001). Inter-observer variability in contouring GTV did not affect the results. ConclusionsDespite the limited numbers, interim analysis of ESCAPE study suggests ERI as a potential predictor of complete response after nCRT for esophageal cancer. Further validation on larger populations is warranted.

The value of apparent diffusion coefficient in predicting treatment response of rectal cancer after neoadjuvant radiation therapy

Objective: To evaluate the value of the apparent diffusion coefficient (ADC) in predicting treatment response in rectal cancer patients after neoadjuvant chemoradiation therapy (nRCT). Subject and method: A study was conducted on 29 rectal cancer patients who underwent nRCT. Patients underwent magnetic resonance imaging (MRI), and tumor volume and ADC values (b-values: 0-1000s/mm²) were measured before and after nRCT. ADC values were measured in two ways: 1. For the entire tumor (ADC1) and 2. On the slice with the largest tumor area (ADC2). Changes in tumor volume (ΔV) and ADC values (ΔADC) before and after treatment were compared with the surgical pathology results, categorized into complete response (pCR)/incomplete response and downstaging T/not downstaging T groups. Result: ΔADC values were higher in the downstaging T group compared to the non-downstaging T group, with ΔADC1 values of 45.4% and 19.1%, respectively (p = 0.005), and ΔADC2 values of 51.7% and 20.2%, respectively (p = 0.01). At the ΔADC1 threshold of 42.2%, sensitivity was 55.6%, and specificity was 100%, while for ΔADC2 at 18.1%, sensitivity and specificity were 88.9% and 72.7%, respectively. There was no statistically significant difference in ΔADC values between the complete response and incomplete response groups (p&gt;0.05). Changes in tumor volume did not provide prognostic value for treatment response to NRCT. Conclusion: The ΔADC value is a potential prognostic factor in predicting treatment response in rectal cancer patients after nRCT, with more accurate diagnostic information compared to tumor volume.

Size and depth of residual tumor after neoadjuvant chemoradiotherapy in rectal cancer - implications for the development of new imaging modalities for response assessment.

With the shift towards organ preserving treatment strategies in rectal cancer it has become increasingly important to accurately discriminate between a complete and good clinical response after neoadjuvant chemoradiotherapy (CRT). Standard of care imaging techniques such as CT and MRI are well equipped for initial staging of rectal tumors, but discrimination between a good clinical and complete response remains difficult due to their limited ability to detect small residual vital tumor fragments. To identify new promising imaging techniques that could fill this gap, it is crucial to know the size and invasion depth of residual vital tumor tissue since this determines the requirements with regard to the resolution and imaging depth of potential new optical imaging techniques. We analyzed 198 pathology slides from 30 rectal cancer patients with a Mandard tumor regression grade 2 or 3 after CRT that underwent surgery. For each patient we determined response pattern, size of the largest vital tumor fragment or bulk and the shortest distance from the vital tumor to the luminal surface. The response pattern was shrinkage in 14 patients and fragmentation in 16 patients. For both groups combined, the largest vital tumor fragment per patient was smaller than 1mm for 38% of patients, below 0.2mm for 12% of patients and for one patient as small as 0.06mm. For 29% of patients the vital tumor remnant was present within the first 0.01mm from the luminal surface and for 87% within 0.5mm. Our results explain why it is difficult to differentiate between a good clinical and complete response in rectal cancer patients using endoscopy and MRI, since in many patients submillimeter tumor fragments remain below the luminal surface. To detect residual vital tumor tissue in all patients included in this study a technique with a spatial resolution of 0.06mm and an imaging depth of 8.9mm would have been required. Optical imaging techniques offer the possibility of detecting majority of these cases due to the potential of both high-resolution imaging and enhanced contrast between tissue types. These techniques could thus serve as a complimentary tool to conventional methods for rectal cancer response assessment.

Identification of Predictive Factors for Pathologic Complete Response in Rectal Cancer Patients Undergoing Neoadjuvant Treatment: A Retrospective Study.

Background: Colorectal cancer is a serious illness, with rectal cancer accounting for thirty percent of all cases. For patients diagnosed with rectal cancer, neoadjuvant downstaging chemoradiotherapy is often necessary due to advanced disease at presentation. However, for certain patients, neoadjuvant chemotherapy can result in a complete response, leading to the possibility of overtreatment during subsequent definitive surgery. Methods: In order to identify predictors for clinical or pathologic complete response, we conducted a retrospective study on 231 patients diagnosed with locally advanced rectal cancer who underwent neoadjuvant treatment. Results: Our results indicate that tumor characteristics remain the primary predictive factors for treatment response in rectal cancer patients. Specifically, we found that a complete pathologic response was more likely in patients with stage I/II disease compared to stage III/IV. However, we did not identify any statistically significant associations between radiotherapy characteristics (such as fractionation, treatment technique or total dose) and complete response rates. Conclusions: In conclusion, our study highlights the importance of tumor stage in predicting pathologic complete response following neoadjuvant chemoradiotherapy for rectal cancer patients. Other clinical and pathologic factors, such as tumor size, may also be important predictors of treatment response and should be explored in future studies.

Artificial intelligence (AI) in the management of colorectal cancer: on the horizon?

In the era of personalized medicine, an ever increasing emphasis is being placed on prognostication and prediction of surgical outcomes and treatment responses in colorectal cancer (CRC).1 Recent advances in information technology have allowed AI integration into clinical practice. Machine learning (ML) is a subfield of AI in which algorithms iteratively ‘learn’ from data to make predictions based on discerned patterns, without being explicitly programmed to do so. Deep learning (DL) is a further subfield of ML, whereby artificial neural networks (ANN) loosely modelled on the human brain structure, are exposed to large or ‘deep’ datasets, deriving predictive capacity from learned patterns in the data.2 Computer vision (CV) refers to the training of computers to read and interpret images, a field closely involved in the last half-decade with DL approaches. Within Radiomics, AI is making an impact in CRC staging.1 DL platforms have demonstrated the ability to distinguish between T2 and T3 rectal cancer on MRI with 90% accuracy,3 and to detect lymph node disease with a higher accuracy than radiologists.4 The combination of histopathological outcomes with MRI and CT imaging shows promise. It allows models to predict a complete pathological response in rectal cancer patients treated with neoadjuvant therapy and the identification of systemic therapy responders with metastatic CRC.1 Colonoscopy is an area in which AI is already being used. AI algorithms designed to aid polyp detection have been shown to increase adenoma detection rates, even amongst experienced endoscopists,5 thus providing a cost effective method of reducing CRC during population screening.6 DL models can both detect and classify polyps with high sensitivity and specificity.7 The development of AI-assisted clinical tools that can provide decision support such as future endoscopic surveillance intervals are ongoing.5 Such tools can also enhance endoscopy training through improved polyp pattern recognition identification.5 Support systems which provide evidence-based clinical decision making tools for CRC are already available. The Watson for Oncology (WFO) program developed by IBM and the Memorial Sloan Kettering Cancer Centre has been trained to follow the U.S. National Comprehensive Cancer Network guidelines.8 Treatment recommendations suggested by the WFO have shown concordance rates of 80%–90% for CRC management compared to a multidisciplinary team (MDT).8, 9 Although not designed to supplant the MDT, AI tools can become an adjunct for clinicians to increase workload time efficiency, and decrease the risk of errors.8 Though still in its infancy, the utilization of AI tools is becoming increasingly relevant within colorectal surgery, through the intraoperative use of CV10 for operative precision and training. Using laparoscopic image data from multiple rectal resections, DL models have been able to recognize the correct phase of the operation, and also act as an intra-operative guide in the identification of complex anatomical structures, such as the total mesorectal excision plane of dissection.10, 11 A recent pilot study has demonstrated the utilization of DL-based software to automate the surgical skill assessment of various steps within a laparoscopic rectal resection, paving its way for future applications in surgical training.12 Despite these exciting developments, many limitations remain. Significant financial investment and creation of regulatory and legal frameworks will be required to integrate AI into existing healthcare systems.13 Furthermore, AI is constrained by the quality and quantity of available data used to train them. As the majority of relevant studies come from only a few geographical locations, AI models are susceptible to inherent biases related to differing patient cohorts.14 AI has the potential to revolutionize the management of CRC. However, there are many challenges to be addressed before AI programs can become embedded into routine care for CRC. Open access publishing facilitated by The University of Melbourne, as part of the Wiley - The University of Melbourne agreement via the Council of Australian University Librarians. Matthew Y. K. Wei: Conceptualization; writing – original draft; writing – review and editing. Junyao Zhang: Writing – review and editing. Reuben Schmidt: Validation; writing – review and editing. Andrew S. Miller: Writing – review and editing. Justin M. C. Yeung: Supervision; validation; writing – review and editing.

PD-0815 Inflammatory markers to predict neoadjuvant chemoradiotherapy response in rectal cancer patients

PD-0815 Inflammatory markers to predict neoadjuvant chemoradiotherapy response in rectal cancer patients

Total Neoadjuvant Therapy Is an Independent Predictor of Complete Pathologic Response in Rectal Cancer Patients

Horesh, Nir MD; Freund, Michael R MD; Garoufalia, Zoe MD; Gefen, Rachel MD; Rizkalla, Sameh Hany Emile MBBCh, MD, FACS; Alvarenga, Emanuela MD; Wexner, Steven D Author Information

Energy Metabolism Is Altered in Radioresistant Rectal Cancer.

Resistance to neoadjuvant chemoradiation therapy is a significant clinical challenge in the management of rectal cancer. There is an unmet need to identify the underlying mechanisms of treatment resistance to enable the development of biomarkers predictive of response and novel treatment strategies to improve therapeutic response. In this study, an in vitro model of inherently radioresistant rectal cancer was identified and characterized to identify mechanisms underlying radioresistance in rectal cancer. Transcriptomic and functional analysis demonstrated significant alterations in multiple molecular pathways, including the cell cycle, DNA repair efficiency and upregulation of oxidative phosphorylation-related genes in radioresistant SW837 rectal cancer cells. Real-time metabolic profiling demonstrated decreased reliance on glycolysis and enhanced mitochondrial spare respiratory capacity in radioresistant SW837 cells when compared to radiosensitive HCT116 cells. Metabolomic profiling of pre-treatment serum samples from rectal cancer patients (n = 52) identified 16 metabolites significantly associated with subsequent pathological response to neoadjuvant chemoradiation therapy. Thirteen of these metabolites were also significantly associated with overall survival. This study demonstrates, for the first time, a role for metabolic reprograming in the radioresistance of rectal cancer in vitro and highlights a potential role for altered metabolites as novel circulating predictive markers of treatment response in rectal cancer patients.

Upregulated Ubiquitin D is a Favorable Prognostic Indicator for Rectal Cancer Patients Undergoing Preoperative Concurrent Chemoradiotherapy.

PurposeFor locally advanced rectal cancer, neoadjuvant concurrent chemoradiotherapy (CCRT) allows tumor downstaging and makes curative radical proctectomy possible. However, we lack a genetic biomarker to predict cancer prognosis or treatment response. We investigated the association between ubiquitin D (UBD) expression and clinical outcomes in rectal cancer patients receiving CCRT.Patients and MethodsWe analyzed the genes associated with the protein modification process (GO:0036211) and identified the UBD gene as the most relevant among the top 7 differentially expressed genes associated with CCRT resistance. We collected tissue specimens from 172 rectal cancer patients who had received CCRT followed by a curative proctectomy. We examine the relationship between UBD expression and patient characteristics, pathological findings, and patient survival, such as metastasis-free survival (MeFS) and disease-specific survival.ResultsUpregulated UBD expression was associated with lower pre-CCRT tumor T stage (P = 0.009), lower post-CCRT tumor T stage (P < 0.001), lower post-CCRT nodal stage (P < 0.001), less vascular invasion (P = 0.015), and better tumor regression (P < 0.001). Using univariate analysis, we found that high UBD expression was correlated with better disease-free survival (DFS) (P < 0.0001), local recurrence-free survival (LRFS) (P < 0.0001) and MeFS (P < 0.0001). Moreover, multivariate analysis demonstrated that high UBD expression was associated with superior DFS (P < 0.001), LRFS (P = 0.01), and MeFS (P = 0.004).ConclusionUBD upregulation was linked to better clinical prognosis, favorable pathological features, and good treatment response in rectal cancer patients undergoing CCRT. These results suggest UBD is a biomarker for rectal cancer.

Tumor Volume Predicts for Pathologic Complete Response in Rectal Cancer Patients Treated With Neoadjuvant Chemoradiation.

Nonoperative management (NOM) of locally advanced rectal cancer is an emerging approach allowing patients to preserve their anal sphincter. Identifying clinical factors associated with pathologic complete response (pCR) is essential for physicians and patients considering NOM. In total, 412 locally advanced rectal cancer patients were included in this retrospective analysis. Tumor volumes were derived from pretreatment MRI. Clinical parameters such as tumor volume, stage, and location were analyzed by univariate and multivariate analysis, against pCR. A receiver operator characteristic curve was generated to identify a tumor volume cut-off with the highest clinically relevant Youden index for predicting pCR. Seventy-five of 412 patients (18%) achieved pCR. A tumor volume threshold of 37.3cm 3 was identified as predictive for pCR. On regression analysis, a tumor volume >37.3cm 3 was associated with a greater than 78% probability of not achieving pCR. On multivariate analysis, a GTV <37.3cm 3 [odds ratio (OR)=3.7, P <0.0001] was significantly associated with an increased pCR rate, whereas tumor length > 4.85cm was associated with pCR on univariate (OR=3.03, P <0.01) but not on multivariate analysis (OR=1.45, P =0.261). Other clinical parameters did not impact pCR rates. A tumor volume threshold of 37.3cm 3 was identified as predictive for pCR in locally advanced rectal cancer patients receiving neoadjuvant chemoradiation. Tumors above this volume threshold corresponded to a greater than 78% probability of not achieving pCR. This information will be helpful at diagnosis for clinicians who are considering potential candidates for NOM.

Association of the collagen signature with pathological complete response in rectal cancer patients.

Collagen in the tumor microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed using multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (163 consecutive patients) was used to validate the model. The collagen signature comprised four collagen features significantly associated with pCR both in the primary and validation cohorts (p < 0.001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors. The nomogram showed good discrimination with area under the ROC curve (AUC) of 0.891 in the primary cohort and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (AUC = 0.908) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. In conclusion, the collagen signature in the tumor microenvironment of pretreatment biopsy is significantly associated with pCR. The nomogram based on the collagen signature and clinicopathological predictors could be used for individualized prediction of pCR in LARC patients before nCRT.

PO-1759 Delta Radiomics can predict complete pathological response in rectal cancer patients

PO-1759 Delta Radiomics can predict complete pathological response in rectal cancer patients

Role of MRI with diffusion and whole tumor volume calculation in detection of response in rectal cancer patients under therapy

Background: MRI examination of the rectum has evolved as the standard technique in the assessment of cancer rectum having multiplaner capabilities and high tissue contrast imaging. The aim of this study: The aim of this study is to evaluate the role of MRI in assessment of rectal neoplasm. Patients and methods: In this study we prospectively evaluated 40 patients (24 male -60%) and (16 females -40%), who referred to the MRI unit, Radiology department, (Minia Oncology Center) for further assessment of histopathology proven rectal carcinoma, during the period of (August 2019 ) to (August 2021). Results: 10% of patients (4 participants) had high rectal cancer while 55% of them (22 patients) had middle rectal cancer. On the other hand, 35% of them (14 patients) had low rectal cancer. 85% of participants (34 patients) were diagnosed as adenocarcinoma while the remaining 6 participants (15% of patients) were diagnosed as mucinous adenocarcinoma. 77.5% of patients (31 participants) had grade II rectal cancer while 9 patients (22.5% of participants) were diagnosed as grade III rectal cancer. On performing TNM staging, we found that the majority of cases; 37.5% of participants (15 patients); were found to be in stage IIA.

Tumor Volume Predicts for Pathological Complete Response in Rectal Cancer Patients Treated With Neoadjuvant Chemoradiation

<h3>Purpose/Objective(s)</h3> Currently, neoadjuvant chemoradiation (CRT) followed by total mesorectal excision is a standard treatment for patients with stage II and III rectal cancer. However, non-operative management of rectal cancer is an emerging approach to allow patients to preserve their anal sphincter. Factors that may help identify patients most likely to have a pathological complete response (pCR) would be helpful for physicians and patients considering a non-operative approach. <h3>Materials/Methods</h3> A total of 377 patients from our institution who had clinical stage II or III rectal cancer treated with neoadjuvant chemoradiation were included in the analysis. Patients were grouped based on their pCR status. Clinical parameters including overall stage, gross tumor volume (GTV), and radiation dose were analyzed by univariate and multivariate analysis, with pCR being the dependent variable. A Receiver Operator Characteristic (ROC) curve was generated in order to identify a tumor volume cutoff with the highest sensitivity for predicting pCR. <h3>Results</h3> The cohort had a median age at diagnosis of 61 years, 68% were male, and 85% presented with stage III disease. 68 of the 377 patients (18%) included in our analysis achieved pCR. A tumor volume of < 18.7 cc is highly sensitive (90%) for predicting pCR. Additionally, on multivariate analysis, a GTV of < 18.7 cc (odds ratio (OR) = 3.1, 95% confidence interval (CI) = 1.613-6.356, <i>P</i> = 0.001) was significantly associated with an increased rate of pCR. Overall stage (<i>P</i> = 0.026) was also significantly associated with pCR, while prescribed dose (<i>P</i> = 0.45) did not appear to impact pCR rates. <h3>Conclusion</h3> Our analysis identified a GTV volume of < 18.7 cc as a strong predictor for pCR in stage II and III rectal cancer patients receiving neoadjuvant chemoradiation, which will be helpful in identifying potential candidates for non-operative management.

29: Tumour Volume Predicts for Pathological Complete Response in Rectal Cancer Patients Treated with Neoadjuvant Chemoradiation

29: Tumour Volume Predicts for Pathological Complete Response in Rectal Cancer Patients Treated with Neoadjuvant Chemoradiation

Association Between Three-Dimensional Transrectal Ultrasound Findings and Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: An Observational Study

BackgroundThere is a significant demand for the development of non-surgical methods for the evaluation of complete response to tumor therapy. Predicting ability and image quality of routine imaging has not been satisfactory. To avoid the deficiencies, we assessed the capability of three-dimensional transrectal ultrasound in predicting the response to neoadjuvant chemoradiotherapy in rectal cancer patients.MethodsThe inclusion criteria were patients with locally advanced rectal adenocarcinoma, receiving capecitabine-based neoadjuvant chemoradiotherapy, distance from anal verge (≤6 cm), clinical stage T3-4 and/or N+ without evidence of distant metastasis, and restaging ycT0-3a (T3a <5 mm) after the end of neoadjuvant chemoradiotherapy. Three-dimensional transrectal ultrasound was performed 7 weeks after neoadjuvant chemoradiotherapy to discern the patients with complete response from the others. Eight main parameters were obtained from three-dimensional transrectal ultrasound: thickness of muscularis on the residual side, thickness of contralateral muscularis, angle of residual arc, regularity of the shape, integrity of the mucosal layer, blurring of the margin, internal echo, and posterior echo. The association between tumor response and three-dimensional transrectal ultrasound parameters was analyzed, and a model was developed by logistic regression.ResultsBetween 2014 and 2019, 101 patients were recruited; 72 cases received total mesorectal excision, and 29 cases underwent watch-and-wait. Among the three-dimensional transrectal ultrasound parameters, the adjusted-thickness of the muscularis (P<0.01), angle of the residual arc (P<0.01), and regularity of the residual shape (P<0.01) were strongly associated with tumor response. In the dataset with total mesorectal excision cases (TME dataset), the residual adjusted-thickness (odds ratio [OR]=4.88, 95% confidence interval [CI]=1.44–16.6, P=0.01) and regularity of the residual shape (OR=5.00, 95% CI=1.13–22.2, P=0.03) were kept in the final logistic model. The area under the curve of the logistic model was 0.84. Among these parameters, residual adjusted-thickness correlated significantly with tumor response. Additionally, we observed similar results in the whole population of 101 cases (whole dataset) and in the cross-validation.ConclusionThree-dimensional transrectal ultrasound model is a valuable method for predicting tumor response in rectal cancer patients undergoing neoadjuvant chemoradiotherapy, which should be included as a factor for evaluating clinical complete response.Trial RegistrationThis trial was registered with ClinicalTrials.gov, number NCT02605265. Registered 9 November 2015 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/record/NCT02605265

Development and Validation of a Collagen Nomogram for Pretreatment Prediction of Pathological Complete Response in Rectal Cancer Patients

Background: Collagen in the tumour microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. Methods: The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed by multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (153 consecutive patients) was used to validate the model. Findings: The collagen signature comprised 4 collagen features significantly associated with pCR both in the two cohorts (P<0·0001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors. The nomogram showed good discrimination with AUC of 0·89 in the primary cohort and good calibration. Good discrimination (AUC = 0·91) and favourable calibration of the nomogram were also confirmed in the validation cohort. Decision curve analysis confirmed the clinical utility of the nomogram. Interpretation: The collagen signature in the tumour microenvironment of pretreatment biopsy is significantly associated with pCR. The nomogram based on the collagen signature and clinicopathological predictors could be used for individualized prediction of pCR in LARC patients before nCRT. Funding Information: This work was supported by grants from the National Natural Science Foundation of China (81773117, 81771881, and 82103041), the State’s Key Project of Research and Development Plan (2019YFE0113700), the Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer (2020B121201004), the Guangdong Provincial Major Talents Project (No.2019JC05Y361), the China Postdoctoral Science Foundation (2020M682789), the Natural Science Foundation of Fujian Province (2018J07004), the Joint Funds of Fujian Provincial Health and Education Research (2019-WJ-21), the Science and Technology Program of Fujian Province (2018Y2003, 2019L3018 and 2019YZ016006), the Clinical Research Startup Program of Southern Medical University by High-level University Construction Funding of Guangdong Provincial Department of Education (LC2016PY010), the Clinical Research Project of Nanfang Hospital (2018CR034, 2020CR001, and 2020CR011), the President Foundation of Nanfang Hospital, Southern Medical University (2019Z023), and the Training Program for Undergraduate Innovation and Entrepreneurship (201912121008, 202012121091 and 202012121277). Declaration of Interests: The authors have no conflicts of interest to declare. Ethics Approval Statement: This study was approved by the Institutional Review Board at Nanfang Hospital of Southern Medical University, Sun Yat-sen University Cancer Center, and Fujian Province Tumor Hospital. Written informed consent was obtained from all participants. The study was conducted in compliance with the Declaration of Helsinki.