Can Haematology Blood Tests at Time of Diagnosis Predict Response to Neoadjuvant Treatment in Locally Advanced Rectal Cancer?

Is It Really Gone? Assessing Response to Neoadjuvant Therapy in Rectal Cancer

PURPOSE

Non-operative management of rectal cancer is a feasible and appealing treatment option for patients who develop a complete response after neoadjuvant therapy. However, identifying patients who are complete responders is often a challenge. This review aims to present and discuss current evidence and recommendations regarding the assessment of treatment response in rectal cancer.

METHODS

A review of the current literature on rectal cancer restaging was performed. Studies included in this review explored the optimal interval between the end of neoadjuvant therapy and restaging, as well as modalities of assessment and their diagnostic performance.

RESULTS

The current standard for restaging rectal cancer is a multimodal assessment with the digital rectal examination, endoscopy, and T2-weighted MRI with diffusion-weighted imaging. Other diagnostic procedures under investigation are PET/MRI, radiomics, confocal laser endomicroscopy, artificial intelligence-assisted endoscopy, cell-free DNA, and prediction models incorporating one or more of the above-mentioned exams.

CONCLUSION

Non-operative management of rectal cancer requires a multidisciplinary approach. Understanding of the robustness and limitations of each exam is critical to inform patient selection for that treatment strategy.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

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.

High Neutrophil-Lymphocyte Ratio, Platelet-Lymphocyte Ratio and Low Lymphocyte Levels Are Correlated With Worse Pathological Complete Response Rates

OBJECTIVE

To investigate the effect of hemogram parameters on predicting pathological complete response (pCR) in locally advanced rectal cancer.

METHODOLOGY

A total of 227 patients with rectal cancer treated with neoadjuvant concurrent chemoradiotherapy (CRT) were retrospectively analyzed. All patients were divided into two subgroups as high or low hemogram parameters according to the cut-off value obtained using the receiver operating characteristic (ROC) curve.

RESULTS

In patients with low neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) levels, pCR rate was statistically significantly higher than the group with high NLR and PLR levels (for NLR: 39.77% vs. 5.34%; p<0.001, for PLR: 32.38% vs 7.01%; p<0.001 respectively). In addition, the pCR rate was significantly better in patients with high lymphocyte levels compared to the group with low lymphocyte levels (33.33% vs. 7.5%; p<0.001, respectively). According to the multivariate logistic regression analysis result, NLR and PLR levels were considered as independent predictors to predict pathological complete response [p<0.001, HR: 0.128 (95% CI=0.051 - 0.322) for NLR; p=0.017, HR: 0.332 (95% CI=0.134 - 0.821) for PLR, respectively].

CONCLUSION

Our study showed that high NLR, PLR, and low lymphocyte levels were correlated with worse pCR rates. In addition to that, NLR and PLR emerged as independent predictive markers.

Can pretreatment blood biomarkers predict pathological response to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer?

Aims: To investigate the value of previously described pretreatment hematological and biochemical biomarkers as predictors of pathological response. Methods: The authors performed a retrospective analysis of 191 patients with locally advanced rectal cancer who underwent long-course neoadjuvant chemoradiotherapy at two Portuguese centers. The authors performed logistic regression analysis to search for predictive markers of pathological complete and good response. Results: High platelet-neutrophil index (p = 0.042) and clinical tumor stage >2 (p = 0.015) were predictive of poor response. None of the analyzed biomarkers predicted pathological complete response in this study. Conclusion: A high platelet-neutrophil index before neoadjuvant chemoradiotherapy could help predict poorer pathological response in patients with locally advanced rectal cancer. However, no other blood biomarker predicted incomplete or poor response in this study.

An inflammation index-based prediction of treatment response to neoadjuvant chemoradiotherapy for rectal mucinous adenocarcinoma

OBJECTIVE

This study aimed to evaluate the predictive value of hematological inflammation-based indexes in the treatment response to neoadjuvant chemoradiotherapy (NCRT) in rectal mucinous adenocarcinomas (MACs).

METHODS

Patients with rectal MACs undergoing NCRT and curative resection were included. Inflammation-based indexes such as systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index (PNI) were calculated. Receiver operator characteristics analysis was used to determine the optimal cutoff points. Multivariable logistic analysis identified predictors of good response to NCRT. A nomogram was developed and validated internally.

RESULTS

A total of 100 patients met the inclusion criteria, with 32 patients developing good response (tumor regression grade, TRG 0 + 1) to NCRT. Lower pre-treatment SII, NLR, and PLR levels were associated with a higher probability of good response to NCRT (P = 0.025, P < 0.001, P = 0.003, respectively), and a higher pre-treatment PNI level was associated with a higher probability of good response to NCRT (P = 0.005). Logistic regression analysis demonstrated that tumor size (< 3 cm, OR = 5.489, P = 0.025), pre-treatment NLR level (< 3.05, OR = 4.025, P = 0.028), pre-treatment PLR level (< 145.98, OR = 4.337, P = 0.038), and pre-treatment PNI level (≥ 41.32, OR = 3.477, P = 0.039) were independent predictors of good response to NCRT. A nomogram was developed with a C-index of 0.827.

CONCLUSION

Hematological inflammation-based indexes, in terms of pre-treatment NLR, PLR, and PNI levels, can help in predicting the treatment response to NCRT for rectal MACs.

Pretreatment blood biomarkers predict pathologic responses to neo-CRT in patients with locally advanced rectal cancer

Aim: To evaluate the value of pretreatment blood biomarkers in predicting pathologic responses to neoadjuvant chemoradiotherapy (neo-CRT) in patients with locally advanced rectal cancer. Materials & methods: We conducted logistic regression analysis and receiver operating characteristic to assess the predictive value of blood biomarkers. The outcome was defined by the pathologic complete response and good response. Results: Carcinoembryonic antigen (CEA) (p < 0.001), neutrophil-to-lymphocyte ratio (p = 0.024), platelet-to-lymphocyte ratio (p = 0.006) and lymphocyte-to-monocyte ratio (LMR) (p < 0.001) were significant predictors of pathologic complete response, with area under the curve of 0.785, 0.794, 0.740 and 0.913, respectively; CEA (p = 0.007) and LMR (p < 0.001) correlated significantly with good response, with area under the curve of 0.743 and 0.771, respectively. Conclusion: Lower LMR and higher CEA, neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio before treatment could predict poorer pathologic response to neo-CRT in patients with locally advanced rectal cancer.