Is It Time to Introduce PET/CT in Rectal Cancer Guidelines?Clin Nucl Med. 2020;45:611-617. [PMID: 32558716 DOI: 10.1097/RLU.0000000000003132]

A 18F-FDG PET/CT based radiomics nomogram for predicting disease-free survival in stage II/III colorectal adenocarcinoma

OBJECTIVES

This study aimed to establish a clinical nomogram model based on a radiomics signatures derived from 18F-fluorodeoxyglucose positron-emission tomography (18F-FDG PET/CT) and clinical parameters to predict disease-free survival (DFS) in patients with stage II/III colorectal adenocarcinoma. Understanding and predicting DFS in these patients is key to optimizing treatment strategies.

METHODS

A retrospective analysis included 332 cases from July 2011 to July 2021 at The Sixth Affiliated Hospital, Sun Yat-sen University, with PET/CT assessing radiomics features and clinicopathological features. Univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox, and multivariable Cox regression identified recurrence-related radiomics features. We used a weighted radiomics score (Rad-score) and independent risk factors to construct a nomogram. Evaluation involved time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

The nomogram, incorporating Rad-score, pN, and pT demonstrated robust predictive ability for DFS in stage II/III colorectal adenocarcinoma. Training cohort areas under the curve (AUCs) were 0.78, 0.80, and 0.86 at 1, 2, and 3 years, respectively, and validation cohort AUCs were 0.79, 0.75, and 0.73. DCA and calibration curves affirmed the nomogram's clinical relevance.

CONCLUSION

The 18F-FDG PET/CT based radiomics nomogram, including Rad-score, pN, and pT, effectively predicted tumor recurrence in stage II/III colorectal adenocarcinoma, significantly enhancing prognostic stratification. Our findings highlight the potential of this nomogram as a guide for clinical decision making to improve patient outcomes.

A scoring system for stratifying the risk of postoperative bone metastases in colorectal cancer

BACKGROUND

Surveillance recommendations for postoperative high-risk colorectal bone metastases patients remain in a gray area of guidelines. We aimed to develop a risk stratification system to select ideal candidates for follow-up of colorectal bone metastases status.

METHODS

Postoperative colorectal cancer patients were included to develop a risk-scoring system to predict bone metastases. Risk scores were calculated based on the predictive factors for bone metastases, which were identified using the Cox proportional hazard regression model. Kaplan-Meier curves visualize the differences between risk groups.

RESULTS

Eight risk factors (age, lymph node metastasis, pathologic tumor deposit, KRAS mutation status, suspicious retroperitoneal lymph node metastasis, lung metastasis status, largest thickness of colorectal cancer lesion, largest short diameter of lymph node) were predictors of colorectal bone metastases and incorporated into the risk scoring system, and the patients were categorized into 2 risk groups. In the low-risk group, the 1, 3, and 5-year colorectal bone metastases rates were 2.4%, 4.6%, and 3.7%, respectively, whereas in the high-risk group, the 1, 3, and 5-year colorectal bone metastases rates were 15.6%, 29.9%, and 44.4%, respectively. The risk scoring system exhibited a C-index of 0.706, 0.795, and 0.841 in 1, 3, and 5 years, respectively. The Kaplan-Meier curve demonstrates that the incidence of colorectal bone metastases was higher in the high-risk group than in the low-risk group (50.5% vs 11.4%, P < .001).

CONCLUSION

This risk-scoring system may be valuable in predicting colorectal bone metastases in colorectal cancer patients, and we suggest that colorectal bone metastases status surveillance be added in the high-risk group.

[18F]FDG-PET-Based Personalized Radiotherapy Dose Prescription

PET imaging with 2'-deoxy-2'-[18F]fluoro-D-glucose ([18F]FDG) has become one of the pillars in the management of malignant diseases. It has proven value in diagnostic workup, treatment policy, follow-up, and as prognosticator for outcome. [18F]FDG is widely available and standards have been developed for PET acquisition protocols and quantitative analyses. More recently, [18F]FDG-PET is also starting to be appreciated as a decision aid for treatment personalization. This review focuses on the potential of [18F]FDG-PET for individualized radiotherapy dose prescription. This includes dose painting, gradient dose prescription, and [18F]FDG-PET guided response-adapted dose prescription. The current status, progress, and future expectations of these developments for various tumor types are discussed.

Preoperative assessment and optimisation for pelvic exenteration in locally advanced and recurrent rectal cancer: A review

The pre-operative phase in planning a pelvic exenteration or extended resections is critical to optimising patient outcomes. This review summarises the key components of preoperative assessment and planning in patients with locally advanced rectal cancer (LARC) and locally recurrent rectal cancer (LLRC) being considered for potential curative resection. The preoperative period can be considered in 5 key phases: 1) Multidisciplinary meeting (MDT) review and recommendation for neoadjuvant therapy and surgery, 2) Anaesthetic preoperative assessment of fitness for surgery and quantification of risk, 3) Shared decision making with the patient and the process of informed consent, 4) Prehabilitation and physiological optimisation 5) Technical aspects of surgical planning. This review will focus on patients who have been recommended for surgery by the MDT and have completed neoadjuvant therapy. Other important considerations beyond the scope of this review are the various neoadjuvant strategies employed which in this patient group include Total Neo-adjuvant Therapy and reirradiation. Critical to improving perioperative outcomes is the dual aim of achieving a negative resection margin in a patient fit enough for extended surgery. Advanced, realistic communication is required pre-operatively and should be maintained throughout recovery. Optimising patient's physiological and psychological reserve with a preoperative prehabilitation programme is important, with physiotherapy, psychological and nutritional input. From a surgical perspective, image based technical preoperative planning is important to identify risk points and ensure correct surgical strategy. Careful attention to the entire patient journey through these 5 preoperative phases can optimise outcomes with the accumulation of marginal gains at multiple timepoints.

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.

Establishment and Clinical Application of an Artificial Intelligence Diagnostic Platform for Identifying Rectal Cancer Tumor Budding

Tumor budding is considered a sign of cancer cell activity and the first step of tumor metastasis. This study aimed to establish an automatic diagnostic platform for rectal cancer budding pathology by training a Faster region-based convolutional neural network (F-R-CNN) on the pathological images of rectal cancer budding. Postoperative pathological section images of 236 patients with rectal cancer from the Affiliated Hospital of Qingdao University, China, taken from January 2015 to January 2017 were used in the analysis. The tumor site was labeled in Label image software. The images of the learning set were trained using Faster R-CNN to establish an automatic diagnostic platform for tumor budding pathology analysis. The images of the test set were used to verify the learning outcome. The diagnostic platform was evaluated through the receiver operating characteristic (ROC) curve. Through training on pathological images of tumor budding, an automatic diagnostic platform for rectal cancer budding pathology was preliminarily established. The precision–recall curves were generated for the precision and recall of the nodule category in the training set. The area under the curve = 0.7414, which indicated that the training of Faster R-CNN was effective. The validation in the validation set yielded an area under the ROC curve of 0.88, indicating that the established artificial intelligence platform performed well at the pathological diagnosis of tumor budding. The established Faster R-CNN deep neural network platform for the pathological diagnosis of rectal cancer tumor budding can help pathologists make more efficient and accurate pathological diagnoses.

Prognostic Value of Baseline Radiomic Features of 18F-FDG PET in Patients with Diffuse Large B-Cell Lymphoma

This study investigates whether baseline ¹⁸F-FDG PET radiomic features can predict survival outcomes in patients with diffuse large B-cell lymphoma (DLBCL). We retrospectively enrolled 83 patients diagnosed with DLBCL who underwent ¹⁸F-FDG PET scans before treatment. The patients were divided into the training cohort (n = 58) and the validation cohort (n = 25). Eighty radiomic features were extracted from the PET images for each patient. Least absolute shrinkage and selection operator regression were used to reduce the dimensionality within radiomic features. Cox proportional hazards model was used to determine the prognostic factors for progression-free survival (PFS) and overall survival (OS). A prognostic stratification model was built in the training cohort and validated in the validation cohort using Kaplan-Meier survival analysis. In the training cohort, run length non-uniformity (RLN), extracted from a gray level run length matrix (GLRLM), was independently associated with PFS (hazard ratio (HR) = 15.7, p = 0.007) and OS (HR = 8.64, p = 0.040). The International Prognostic Index was an independent prognostic factor for OS (HR = 2.63, p = 0.049). A prognostic stratification model was devised based on both risk factors, which allowed identification of three risk groups for PFS and OS in the training (p < 0.001 and p < 0.001) and validation (p < 0.001 and p = 0.020) cohorts. Our results indicate that the baseline ¹⁸F-FDG PET radiomic feature, RLN_GLRLM, is an independent prognostic factor for survival outcomes. Furthermore, we propose a prognostic stratification model that may enable tailored therapeutic strategies for patients with DLBCL.