Mucinous Adenocarcinoma Predicts Poor Response and Prognosis in Patients With Locally Advanced Rectal Cancer: A Pooled Analysis of Individual Participant Data From 3 Prospective Studies

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

Adjuvant chemotherapy improves long-term survival in pathologic stage III rectal mucinous adenocarcinoma after pre-operative chemoradiotherapy

PURPOSE

The benefits of adjuvant chemotherapy remain debated rectal mucinous adenocarcinoma (MC). Our study aims to delve into the efficacy of adjuvant chemotherapy in pathologic stage III rectal MC by a large population-based database.

METHODS

The Chi-square test was performed to examine the parameters between treatment groups. The overall survival (OS) and cancer-specific survival (CSS) of treatment groups were conducted by using the Kaplan-Meier method. The impact of factors on survival was assessed using Cox regression analyses. To balance the covariates and reduce the selection bias, we employed propensity score matching (PSM) to narrow the differences between treatment groups.

RESULTS

The median follow-up time for overall patients was 80 months. In the pre-operative chemoradiotherapy (pre-CRT) group, patients who received adjuvant chemotherapy had significantly better 5-year OS and CSS. Multivariate analyses found that adjuvant chemotherapy was associated with better OS (p < 0.001, HR (95% CI): 0.66 (0.51-0.86)) and CSS (p = 0.012, HR (95% CI): 0.71 (0.54-0.93)). However, adjuvant chemotherapy was not an independent prognosis factor in both OS (p = 0.149, HR (95% CI): 0.76 (0.53-1.1); Supplement Table 1) and CSS (p = 0.183, HR (95% CI): 0.74 (0.48-1.15)) in patients who did not receive pre-CRT. After PSM, similar results were found in the pre-CRT and the no pre-CRT groups.

CONCLUSION

In conclusion, our population-based retrospective cohort study indicates that the effects of adjuvant chemotherapy were associated with the pre-CRT status in patients with stage III rectal MC. In patients who underwent pre-CRT, the receipt of adjuvant chemotherapy was associated with better survival outcomes. Conversely, adjuvant chemotherapy does not seem to confer significant survival benefits to patients without pre-CRT.

Radiomics from Mesorectal Blood Vessels and Lymph Nodes: A Novel Prognostic Predictor for Rectal Cancer with Neoadjuvant Therapy

The objective of our study is to investigate the predictive value of various combinations of radiomic features from intratumoral and different peritumoral regions of interest (ROIs) for achieving a good pathological response (pGR) following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study was conducted using data from LARC patients who underwent nCRT between 2013 and 2021. Patients were divided into training and validation cohorts at a ratio of 4:1. Intratumoral ROIs (ROI_ITU) were segmented on T2-weighted imaging, while peritumoral ROIs were segmented using two methods: ROI_{PTU_2mm}, ROI_{PTU_4mm}, and ROI_{PTU_6mm}, obtained by dilating the boundary of ROI_ITU by 2 mm, 4 mm, and 6 mm, respectively; and ROI_{MR_F} and ROI_{MR_BVLN}, obtained by separating the fat and blood vessels + lymph nodes in the mesorectum. After feature extraction and selection, 12 logistic regression models were established using radiomics features derived from different ROIs or ROI combinations, and five-fold cross-validation was performed. The average area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. The study included 209 patients, consisting of 118 pGR and 91 non-pGR patients. The model that integrated ROI_ITU and ROI_{MR_BVLN} features demonstrated the highest predictive ability, with an AUC (95% confidence interval) of 0.936 (0.904-0.972) in the training cohort and 0.859 (0.745-0.974) in the validation cohort. This model outperformed models that utilized ROI_ITU alone (AUC = 0.779), ROI_{MR_BVLN} alone (AUC = 0.758), and other models. The radscore derived from the optimal model can predict the treatment response and prognosis after nCRT. Our findings validated that the integration of intratumoral and peritumoral radiomic features, especially those associated with mesorectal blood vessels and lymph nodes, serves as a potent predictor of pGR to nCRT in patients with LARC. Pending further corroboration in future research, these insights could provide novel imaging markers for refining therapeutic strategies.

Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields.

Pretreatment "prognostic nutritional index" as an indicator of outcome in lung cancer patients receiving ICI-based treatment: Systematic review and meta-analysis

BACKGROUND

The pretreatment prognostic nutritional index (PNI) is an indicator of nutritional and immune status, and has potential use as a predictor of survival in cancer patients. Several retrospective studies have used the PNI to predict the outcome of lung cancer patients receiving different immune checkpoint inhibitors (ICIs), but the results have been inconsistent. The objective of our study is to assess the relationship of pretreatment PNI with survival outcomes in lung cancer patients who received ICI-based treatments by meta-analysis.

METHODS

We searched the EMBASE, PubMed, Cochrane Library, American Society of Clinical Oncology, and European Society of Medical Oncology databases to identify studies that reported overall survival (OS) or progression-free survival (PFS) in eligible patients. Eight studies were eligible based on predefined inclusion and exclusion criteria. Data and pooled indicators were extracted from these studies. Meta-analysis was used to analyze hazard ratios (HRs) and 95% confidence intervals (CIs) for OS and/or PFS and the prognostic value of pretreatment PNI. We completed the registration of the research protocol (Registration number: INPLASY202240087, DOI number: 10.37766/inplasy2022.4.0087).

RESULTS

We analyzed data from 8 eligible studies (831 patients). Meta-analysis showed that relative to patients with low pretreatment PNI, those with a high pretreatment PNI had better OS (HR = 2.50, 95% CI = 1.44-4.33, P = .001) and better PFS (HR = 1.94, 95% CI = 1.56-2.42, P < .001). Sensitivity analysis indicated these results were robust. There was also no evidence of publication bias.

CONCLUSION

Lung cancer patients receiving ICI-based treatments who had higher pretreatment PNI had better OS and PFS.