Diagnostic performance of the Japanese Narrow-band imaging expert team classification system using dual focus magnification in real-time Vietnamese setting.

The JNET classification, combined with magnified narrowband imaging (NBI), is essential for predicting the histology of colorectal polyps and guiding personalized treatment strategies. Despite its recognized utility, the diagnostic efficacy of JNET classification using NBI with dual focus (DF) magnification requires exploration in the Vietnamese context. This study aimed to investigate the diagnostic performance of the JNET classification with the NBI-DF mode in predicting the histology of colorectal polyps in Vietnam. A cross-sectional study was conducted at the University Medical Center in Ho Chi Minh City, Vietnam. During real-time endoscopy, endoscopists evaluated the lesion characteristics and recorded optical diagnoses using the dual focus mode magnification according to the JNET classification. En bloc lesion resection (endoscopic or surgical) provided the final pathology, serving as the reference standard for optical diagnoses. A total of 739 patients with 1353 lesions were recruited between October 2021 and March 2023. The overall concordance with the JNET classification was 86.9%. Specificities and positive predictive values for JNET types were: type 1 (95.7%, 88.3%); type 2A (81.4%, 90%); type 2B (96.6%, 54.7%); and type 3 (99.9%, 93.3%). The sensitivity and negative predictive value for differentiating neoplastic from non-neoplastic lesions were 97.8% and 88.3%, respectively. However, the sensitivity for distinguishing malignant from benign neoplasia was lower at 64.1%, despite a specificity of 95.9%. Notably, the specificity and positive predictive value for identifying deep submucosal cancer were high at 99.8% and 93.3%. In Vietnam, applying the JNET classification with NBI-DF demonstrates significant value in predicting the histology of colorectal polyps. This classification guides treatment decisions and prevents unnecessary surgeries.

DHAFormer: Dual-channel hybrid attention network with transformer for polyp segmentation.

The accurate early diagnosis of colorectal cancer significantly relies on the precise segmentation of polyps in medical images. Current convolution-based and transformer-based segmentation methods show promise but still struggle with the varied sizes and shapes of polyps and the often low contrast between polyps and their background. This research introduces an innovative approach to confronting the aforementioned challenges by proposing a Dual-Channel Hybrid Attention Network with Transformer (DHAFormer). Our proposed framework features a multi-scale channel fusion module, which excels at recognizing polyps across a spectrum of sizes and shapes. Additionally, the framework's dual-channel hybrid attention mechanism is innovatively conceived to reduce background interference and improve the foreground representation of polyp features by integrating local and global information. The DHAFormer demonstrates significant improvements in the task of polyp segmentation compared to currently established methodologies.

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis.

BACKGROUND: Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data. METHODS: Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models' diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted. RESULTS: Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022-2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78-0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001). CONCLUSION: Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.

[Retroperitoneal schwannoma mimicking colorectal cancer metastases: a false positive result report.] / Schwannoma retroperitoneal simulando metástasis de cáncer colorrectal: reporte de un resultado falso positivo.

Introduction: schwannomas are benign and common soft tissue tumors. They are usually asymptomatic and are discovered for other reasons. Materials: we present the case of an 82-year-old male patient with a recent diagnosis of moderately differentiated adenocarcinoma of the colon and a hypermetabolic periaortic nodule as an incidental finding. Results: percutaneous biopsy of the periaortic nodule confirmed the diagnosis of schwannoma. At one year of follow-up, growth of the schwannoma has been demonstrated. There are no signs of progression of his oncological disease. Conclusions: schwannomas are benign tumors, rarely found in the retroperitoneum and can be sources of false-positive positron emission tomography results.

Introducción: los schwannomas son tumores benignos y frecuentes de las partes blandas. Habitualmente son asintomáticos y son descubiertos por otros motivos. Materiales y métodos: presentamos el caso de un paciente masculino de 82 años con diagnóstico reciente de adenocarcinoma de colon moderadamente diferenciado y con un nódulo periaórtico hipermetabólico como hallazgo incidental. Resultados: la biopsia percutánea del nódulo periaórtico confirmó el diagnóstico de schwannoma. Al año de seguimiento, se ha demostrado crecimiento del schwannoma. No hay signos de progresión de su enfermedad oncológica. Conclusión: los schwannomas son tumores benignos, infrecuentes en el retroperitoneo y pueden ser fuentes de resultados falsos positivos en tomografía por emisión de positrones.

AI-Assisted Body Composition Assessment Using CT Imaging in Colorectal Cancer Patients: Predictive Capacity for Sarcopenia and Malnutrition Diagnosis.

(1) Background: The assessment of muscle mass is crucial in the nutritional evaluation of patients with colorectal cancer (CRC), as decreased muscle mass is linked to increased complications and poorer prognosis. This study aims to evaluate the utility of AI-assisted L3 CT for assessing body composition and determining low muscle mass using both the Global Leadership Initiative on Malnutrition (GLIM) criteria for malnutrition and the European Working Group on Sarcopenia in Older People (EWGSOP2) criteria for sarcopenia in CRC patients prior to surgery. Additionally, we aim to establish cutoff points for muscle mass in men and women and propose their application in these diagnostic frameworks. (2) Methods: This retrospective observational study included CRC patients assessed by the Endocrinology and Nutrition services of the Regional University Hospitals of Malaga, Virgen de la Victoria of Malaga, and Vall d'Hebrón of Barcelona from October 2018 to July 2023. A morphofunctional assessment, including anthropometry, bioimpedance analysis (BIA), and handgrip strength, was conducted to apply the GLIM criteria for malnutrition and the EWGSOP2 criteria for sarcopenia. Body composition evaluation was performed through AI-assisted analysis of CT images at the L3 level. ROC analysis was used to determine the predictive capacity of variables derived from the CT analysis regarding the diagnosis of low muscle mass and to describe cutoff points. (3) Results: A total of 586 patients were enrolled, with a mean age of 68.4 ± 10.2 years. Using the GLIM criteria, 245 patients (41.8%) were diagnosed with malnutrition. Applying the EWGSOP2 criteria, 56 patients (9.6%) were diagnosed with sarcopenia. ROC curve analysis for the skeletal muscle index (SMI) showed a strong discriminative capacity of muscle area to detect low fat-free mass index (FFMI) (AUC = 0.82, 95% CI 0.77-0.87, p < 0.001). The identified SMI cutoff for diagnosing low FFMI was 32.75 cm2/m2 (Sn 77%, Sp 64.3%; AUC = 0.79, 95% CI 0.70-0.87, p < 0.001) in women, and 39.9 cm2/m2 (Sn 77%, Sp 72.7%; AUC = 0.85, 95% CI 0.80-0.90, p < 0.001) in men. Additionally, skeletal muscle area (SMA) showed good discriminative capacity for detecting low appendicular skeletal muscle mass (ASMM) (AUC = 0.71, 95% CI 0.65-0.76, p < 0.001). The identified SMA cutoff points for diagnosing low ASMM were 83.2 cm2 (Sn 76.7%, Sp 55.3%; AUC = 0.77, 95% CI 0.69-0.84, p < 0.001) in women and 112.6 cm2 (Sn 82.3%, Sp 58.6%; AUC = 0.79, 95% CI 0.74-0.85, p < 0.001) in men. (4) Conclusions: AI-assisted body composition assessment using CT is a valuable tool in the morphofunctional evaluation of patients with colorectal cancer prior to surgery. CT provides quantitative data on muscle mass for the application of the GLIM criteria for malnutrition and the EWGSOP2 criteria for sarcopenia, with specific cutoff points established for diagnostic use.

CT-determined low skeletal muscle index predicts poor prognosis in patients with colorectal cancer.

BACKGROUND: Sarcopenia is highly prevalent among patients with colorectal cancer (CRC). Computed tomography (CT)-based assessment of low skeletal muscle index (SMI) is widely used for diagnosing sarcopenia. However, there are conflicting findings on the association between low SMI and overall survival (OS) in CRC patients. The objective of this study was to investigate whether CT-determined low SMI can serve as a valuable prognostic factor in CRC. METHODS: We collected data from patients with CRC who underwent radical surgery at our institution between June 2020 and November 2021. The SMI at the third lumbar vertebra was calculated using CT scans, and the cutoff values for defining low SMI were determined using receiver operating characteristic curves. Univariate and multivariate analyses were performed to assess the associations between clinical characteristics and postoperative major complications. RESULTS: A total of 464 patients were included in the study, 229 patients (46.7%) were classified as having low SMI. Patients with low SMI were older and had a lower body mass index (BMI), a higher neutrophil to lymphocyte ratio (NLR), and higher nutritional risk screening 2002 (NRS2002) scores compared to those with normal SMI. Furthermore, patients with sarcopenia had a higher rate of major complications (10.9% vs. 1.3%; p < 0.001) and longer length of stay (9.09 ± 4.86 days vs. 8.25 ± 3.12 days; p = 0.03). Low SMI and coronary heart disease were identified as independent risk factors for postoperative major complications. Moreover, CRC patients with low SMI had significantly worse OS. Furthermore, the combination of low SMI with older age or TNM stage II + III resulted in the worst OS in each subgroup analysis. CONCLUSIONS: CT-determined low SMI is associated with poor prognosis in patients with CRC, especially when combined with older age or advanced TNM stage.

Fluorescence excitation-scanning hyperspectral imaging with scalable 2D-3D deep learning framework for colorectal cancer detection.

Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians.

Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study.

BACKGROUND: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. METHODS: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists' detection performance. FINDINGS: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists. INTERPRETATION: The developed DL model can accurately detect colorectal cancer and improve radiologists' detection performance, showing its potential as an effective computer-aided detection tool. FUNDING: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

Use of 3D-CT-derived psoas major muscle volume in defining sarcopenia in colorectal cancer.

BACKGROUND: Sarcopenia is characterized by reduced skeletal muscle volume and is a condition that is prevalent among elderly patients and associated with poor prognosis as a comorbidity in malignancies. Given the aging population over 80 years old in Japan, an understanding of malignancies, including colorectal cancer (CRC), complicated by sarcopenia is increasingly important. Therefore, the focus of this study is on a novel and practical diagnostic approach of assessment of psoas major muscle volume (PV) using 3-dimensional computed tomography (3D-CT) in diagnosis of sarcopenia in patients with CRC. METHODS: The subjects were 150 patients aged ≥ 80 years with CRC who underwent primary tumor resection at Juntendo University Hospital between 2004 and 2017. 3D-CT measurement of PV and conventional CT measurement of the psoas major muscle cross-sectional area (PA) were used to identify sarcopenia (group S) and non-sarcopenia (group nS) cases. Clinicopathological characteristics, operative results, postoperative complications, and prognosis were compared between these groups. RESULTS: The S:nS ratios were 15:135 for the PV method and 52:98 for the PA method. There was a strong positive correlation (r = 0.66, p < 0.01) between PVI (psoas major muscle volume index) and PAI (psoas major muscle cross-sectional area index), which were calculated by dividing PV or PA by the square of height. Surgical results and postoperative complications did not differ significantly in the S and nS groups defined using each method. Overall survival was worse in group S compared to group nS identified by PV (p < 0.01), but not significantly different in groups S and nS identified by PA (p = 0.77). A Cox proportional hazards model for OS identified group S by PV as an independent predictor of a poor prognosis (p < 0.05), whereas group S by PA was not a predictor of prognosis (p = 0.60). CONCLUSIONS: The PV method for identifying sarcopenia in elderly patients with CRC is more practical and sensitive for prediction of a poor prognosis compared to the conventional method.

BATF promotes tumor progression and association with FDG PET-derived parameters in colorectal cancer.

PURPOSE: The purpose of the study was to evaluate the expression and function of basic leucine zipper ATF-like transcription factor (BATF) in colorectal cancer (CRC), and its correlation with 2-deoxy-2[18F]fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) parameters. METHODS: The TIMER database, GEPIA database, TCGA, and GEO database were used to analyze the expression profile of BATF in human cancers. The reverse transcription­quantitative PCR and western blot analyses were used to evaluate the mRNA level and protein expression in different CRC cell lines. The expression of BATF in SW620 and HCT116 cells was silenced and cell counting kit-8 assays and clonogenic assay were utilized to evaluate the role of BATF in CRC proliferation. The expression of tumor BATF and glucose transporter 1 (GLUT-1) were examined using immunohistochemical tools in 37 CRC patients undergoing preoperative 18F-FDG PET/CT imaging. The correlation between the PET/CT parameters and immunohistochemical result was evaluated. RESULTS: In database, BATF was highly expressed in pan-cancer analyses, including CRC, and was associated with poor prognosis in CRC. In vitro, the results showed that knocking down of BATF expression could inhibit the proliferation of SW620 and HCT116 cells. In CRC patients, BATF expression was upregulated in tumor tissues compared with matched para-tumoral tissues, and was related with gender and Ki-67 levels. BATF expression was positively related to GLUT-1 expression and PET/CT parameters, including tumor size, maximum standard uptake value, metabolic tumor volume, and total lesion glycolysis. The multiple logistic analyses showed that SUVmax was an independent predictor of BATF expression. With 15.96 g/cm3 as the cutoff, sensitivity was 85.71%, specificity 82.61%, and area-under-the-curve 0.854. CONCLUSION: BATF may be an oncogene associated with 18F-FDG PET/CT parameters in CRC. SUVmax may be an independent predictor of BATF expression.

Predictive value of 18F-fluorodeoxyglucose PET/CT in histological grade of incidental colorectal adenoma.

BACKGROUND: 18F-fluorodeoxyglucose (18F-FDG) positron-emission tomography/computed tomography (PET/CT) as an imaging modality for the whole body has shown its value in detecting incidental colorectal adenoma. In clinical practice, adenomatous polyps can be divided into three groups: low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN) and cancer, which can lead to different clinical management. However, the relationship between the 18F-FDG PET/CT SUVmax and the histological grade of adenomatous polyps is still not established, which is a challenging but valuable task. METHODS: This retrospective study included 255 patients with colorectal adenoma (CRA) or colorectal adenocarcinomas (AC) who had corresponding 18F-FDG uptake incidentally found on PET/CT. The correlations of SUVmax with pathological characteristics and tumor size were assessed. Neoplasms were divided into LGIN, HGIN, and AC according to histological grade. Receiver operating characteristic (ROC) analysis was applied to evaluate the predictive value of the SUVmax-only model and comprehensive models which were established with imaging and clinical predictors identified by univariate and multivariate analysis. RESULTS: The SUVmax was positively correlated with histological grades (r=0.529, P<0.001). Univariate and multivariate analysis showed that SUVmax was an independent risk factor among all groups except between HGIN and AC. The area under the curves (AUCs) of the comprehensive model for distinguishing between AC and adenoma, LGIN and HIGN, LGIN and AC, and HGIN and AC were 0.886, 0.780, 0.945, 0.733, respectively, which is statistically higher than the AUCs of the SUVmax-only model with 0.812, 0.733, 0.863, and 0.688, respectively. CONCLUSIONS: As an independent risk factor, SUVmax based on 18F-FDG PET/CT is highly associated with the histological grade of CRA. Thus, 18F-FDG PET/CT can serve as a noninvasive tool for precise diagnosis and assist in the preoperative formulation of treatment strategies for patients with incidental CRA.

Standard-definition White-light, High-definition White-light versus Narrow-band Imaging Endoscopy for Detecting Colorectal Adenomas: A Multicenter Randomized Controlled Trial.

OBJECTIVE: This study aimed to compare the performance of standard-definition white-light endoscopy (SD-WL), high-definition white-light endoscopy (HD-WL), and high-definition narrow-band imaging (HD-NBI) in detecting colorectal lesions in the Chinese population. METHODS: This was a multicenter, single-blind, randomized, controlled trial with a non-inferiority design. Patients undergoing endoscopy for physical examination, screening, and surveillance were enrolled from July 2017 to December 2020. The primary outcome measure was the adenoma detection rate (ADR), defined as the proportion of patients with at least one adenoma detected. The associated factors for detecting adenomas were assessed using univariate and multivariate logistic regression. RESULTS: Out of 653 eligible patients enrolled, data from 596 patients were analyzed. The ADRs were 34.5% in the SD-WL group, 33.5% in the HD-WL group, and 37.5% in the HD-NBI group (P=0.72). The advanced neoplasm detection rates (ANDRs) in the three arms were 17.1%, 15.5%, and 10.4% (P=0.17). No significant differences were found between the SD group and HD group regarding ADR or ANDR (ADR: 34.5% vs. 35.6%, P=0.79; ANDR: 17.1% vs. 13.0%, P=0.16, respectively). Similar results were observed between the HD-WL group and HD-NBI group (ADR: 33.5% vs. 37.7%, P=0.45; ANDR: 15.5% vs. 10.4%, P=0.18, respectively). In the univariate and multivariate logistic regression analyses, neither HD-WL nor HD-NBI led to a significant difference in overall adenoma detection compared to SD-WL (HD-WL: OR 0.91, P=0.69; HD-NBI: OR 1.15, P=0.80). CONCLUSION: HD-NBI and HD-WL are comparable to SD-WL for overall adenoma detection among Chinese outpatients. It can be concluded that HD-NBI or HD-WL is not superior to SD-WL, but more effective instruction may be needed to guide the selection of different endoscopic methods in the future. Our study's conclusions may aid in the efficient allocation and utilization of limited colonoscopy resources, especially advanced imaging technologies.

Synthesis and Evaluation of a Novel c-Met-Targeting Cyclic Peptide as a Potential Diagnostic Agent for Colorectal Cancer.

The mesenchymal-epithelial transition factor (c-Met) is a receptor tyrosine kinase linked to the proliferation, survival, invasion, and metastasis of several types of cancers, including colorectal cancer (CRC), particularly when aberrantly activated. Our study strategically designs peptides derived from interactions between c-Met and the antibody Onartuzumab. By utilizing a cyclic strategy, we achieved significantly enhanced peptide stability and affinity. Our in vitro assessments confirmed that the cyclic peptide HYNIC-cycOn exhibited a higher affinity (KD = 83.5 nM) and greater specificity compared with its linear counterpart. Through in vivo experiments, [99mTc]Tc-HYNIC-cycOn displayed exceptional tumor-targeting capabilities and minimal absorption in nontumor cells, as confirmed by single-photon emission computed tomography. Notably, the ratios of tumor to muscle and tumor to intestine, 1 h postinjection, were 4.78 ± 0.86 and 3.24 ± 0.47, respectively. Comparable ratios were observed in orthotopic CRC models, recording 4.94 ± 0.32 and 3.88 ± 0.41, respectively. In summary, [99mTc]Tc-HYNIC-cycOn shows substantial promise as a candidate for clinical applications. We show that [99mTc]Tc-HYNIC-cycOn can effectively target and visualize c-Met-expressing tumors in vivo, providing a promising approach for enhancing diagnostic accuracy when detecting c-Met in CRC.

Prediction of lymph node metastasis in T1 colorectal cancer based on combination of body composition and vascular invasion.

OBJECTIVES: Lymph node metastasis (LNM) in colorectal cancer (CRC) patients is not only associated with the tumor's local pathological characteristics but also with systemic factors. This study aims to assess the feasibility of using body composition and pathological features to predict LNM in early stage colorectal cancer (eCRC) patients. METHODS: A total of 192 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in the study. The cross-sectional areas of skeletal muscle, subcutaneous fat, and visceral fat at the L3 vertebral body level in CT scans were measured using Image J software. Logistic regression analysis were conducted to identify the risk factors for LNM. The predictive accuracy and discriminative ability of the indicators were evaluated using receiver operating characteristic (ROC) curves. Delong test was applied to compare area under different ROC curves. RESULTS: LNM was observed in 32 out of 192 (16.7%) patients with eCRC. Multivariate analysis revealed that the ratio of skeletal muscle area to visceral fat area (SMA/VFA) (OR = 0.021, p = 0.007) and pathological indicators of vascular invasion (OR = 4.074, p = 0.020) were independent risk factors for LNM in eCRC patients. The AUROC for SMA/VFA was determined to be 0.740 (p < 0.001), while for vascular invasion, it was 0.641 (p = 0.012). Integrating both factors into a proposed predictive model resulted in an AUROC of 0.789 (p < 0.001), indicating a substantial improvement in predictive performance compared to relying on a single pathological indicator. CONCLUSION: The combination of the SMA/VFA ratio and vascular invasion provides better prediction of LNM in eCRC.

Phase I trial of single-photon emission computed tomography-guided liver-directed radiotherapy for patients with low functional liver volume.

BACKGROUND: Traditional constraints specify that 700 cc of liver should be spared a hepatotoxic dose when delivering liver-directed radiotherapy to reduce the risk of inducing liver failure. We investigated the role of single-photon emission computed tomography (SPECT) to identify and preferentially avoid functional liver during liver-directed radiation treatment planning in patients with preserved liver function but limited functional liver volume after receiving prior hepatotoxic chemotherapy or surgical resection. METHODS: This phase I trial with a 3 + 3 design evaluated the safety of liver-directed radiotherapy using escalating functional liver radiation dose constraints in patients with liver metastases. Dose-limiting toxicities were assessed 6-8 weeks and 6 months after completing radiotherapy. RESULTS: All 12 patients had colorectal liver metastases and received prior hepatotoxic chemotherapy; 8 patients underwent prior liver resection. Median computed tomography anatomical nontumor liver volume was 1584 cc (range = 764-2699 cc). Median SPECT functional liver volume was 1117 cc (range = 570-1928 cc). Median nontarget computed tomography and SPECT liver volumes below the volumetric dose constraint were 997 cc (range = 544-1576 cc) and 684 cc (range = 429-1244 cc), respectively. The prescription dose was 67.5-75 Gy in 15 fractions or 75-100 Gy in 25 fractions. No dose-limiting toxicities were observed during follow-up. One-year in-field control was 57%. One-year overall survival was 73%. CONCLUSION: Liver-directed radiotherapy can be safely delivered to high doses when incorporating functional SPECT into the radiation treatment planning process, which may enable sparing of lower volumes of liver than traditionally accepted in patients with preserved liver function. TRIAL REGISTRATION: NCT02626312.

[18F]FMISO-PET imaging reveals the role of hypoxia severity in checkpoint blockade response.

CONTEXT: Hypoxia within the tumor microenvironment is a critical factor influencing the efficacy of immunotherapy, including immune checkpoint inhibition. Insufficient oxygen supply, characteristic of hypoxia, has been recognized as a central determinant in the progression of various cancers. The reemergence of evofosfamide, a hypoxia-activated prodrug, as a potential treatment strategy has sparked interest in addressing the role of hypoxia in immunotherapy response. This investigation sought to understand the kinetics and heterogeneity of tumor hypoxia and their implications in affecting responses to immunotherapeutic interventions with and without evofosfamide. PURPOSE: This study aimed to investigate the influence of hypoxia on immune checkpoint inhibition, evofosfamide monotherapy, and their combination on colorectal cancer (CRC). Employing positron emission tomography (PET) imaging, we developed novel analytical methods to quantify and characterize tumor hypoxia severity and distribution. PROCEDURES: Murine CRC models were longitudinally imaged with [18F]-fluoromisonidazole (FMISO)-PET to quantify tumor hypoxia during checkpoint blockade (anti-CTLA-4 + and anti-PD1 +/- evofosfamide). Metrics including maximum tumor [18F]FMISO uptake (FMISOmax) and mean tumor [18F]FMISO uptake (FMISOmean) were quantified and compared with normal muscle tissue (average muscle FMISO uptake (mAvg) and muscle standard deviation (mSD)). Histogram distributions were used to evaluate heterogeneity of tumor hypoxia. FINDINGS: Severe hypoxia significantly impeded immunotherapy effectiveness consistent with an immunosuppressive microenvironment. Hypoxia-specific PET imaging revealed a striking degree of spatial heterogeneity in tumor hypoxia, with some regions exhibiting significantly more severe hypoxia than others. The study identified FMISOmax as a robust predictor of immunotherapy response, emphasizing the impact of localized severe hypoxia on tumor volume control during therapy. Interestingly, evofosfamide did not directly reduce hypoxia but markedly improved the response to immunotherapy, uncovering an alternative mechanism for its efficacy. CONCLUSIONS: These results enhance our comprehension of the interplay between hypoxia and immune checkpoint inhibition within the tumor microenvironment, offering crucial insights for the development of personalized cancer treatment strategies. Non-invasive hypoxia quantification through molecular imaging evaluating hypoxia severity may be an effective tool in guiding treatment planning, predicting therapy response, and ultimately improving patient outcomes across diverse cancer types and tumor microenvironments. It sets the stage for the translation of these findings into clinical practice, facilitating the optimization of immunotherapy regimens by addressing tumor hypoxia and thereby enhancing the efficacy of cancer treatments.

Resection of colorectal carcinoma liver metastases: A population-based study in outcomes and factors associated with recurrent disease.

OBJECTIVE: To assess the hepatic disease-free survival (HDFS) and overall survival (OS) of patients who underwent resection of colorectal cancer liver metastases (CRCLM) in our population, and evaluate what factors are associated with these outcomes. METHODS: Patients with resected non-mucinous CRCLM between January 2013-February 2020 were retrospectively identified. Dates of diagnosis, surgery, and, if applicable, death were recorded. HDFS and OS were calculated using a census date of 24 September 2022. Separate Cox multivariate regression analyses were performed to evaluate for association between HDFS and OS and the following factors: pre-operative imaging interval (<4 weeks vs. ≥4 weeks); pre-operative imaging modality (CT only vs. MRI+CT); extrahepatic disease at time of hepatectomy (yes vs. no); tumor burden score (TBS, where TBS2 = (largest axial dimension of CRCLM)2 + (number of CRCLM)2); pT and pN; and neoadjuvant chemotherapy. RESULTS: 137 subjects (mean age, 61 ± 11 years, 86 males) were included. Associations with recurrent hepatic disease were found with chemotherapy (HR 2.11[95 % CI = 1.13-3.92]), TBS (HR 1.30[95 % CI = 1.17-1.45]), MRI+CT (HR 2.12[95 % CI = 1.29-3.48]), and extrahepatic disease at hepatectomy (HR 2.16[95 % CI = 1.08-4.35]). For mortality, associations were found with TBS (HR 1.22[95 % CI = 1.09-1.37]), pT (HR 1.45[95 % CI = 1.05-2.00]), and extrahepatic disease at hepatectomy (HR 2.10[95 % CI = 1.31-3.36]). CONCLUSION: In our population, non-imaging related factors TBS, neoadjuvant chemotherapy, pT and presence of extrahepatic disease at time of hepatectomy were associated with HDFS and/or OS. The preoperative imaging interval and use of preoperative MRI were not associated with improved patient outcomes.

Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.

BACKGROUND AND OBJECTIVES: Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. METHODS: Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. RESULTS: Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. CONCLUSIONS: Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.

Preoperative MRI radiomic analysis for predicting local tumor progression in colorectal liver metastases before microwave ablation.

PURPOSE: Radiomics may aid in predicting prognosis in patients with colorectal liver metastases (CLM). Consistent data is available on CT, yet limited data is available on MRI. This study assesses the capability of MRI-derived radiomic features (RFs) to predict local tumor progression-free survival (LTPFS) in patients with CLMs treated with microwave ablation (MWA). METHODS: All CLM patients with pre-operative Gadoxetic acid-MRI treated with MWA in a single institution between September 2015 and February 2022 were evaluated. Pre-procedural information was retrieved retrospectively. Two observers manually segmented CLMs on T2 and T1-Hepatobiliary phase (T1-HBP) scans. After inter-observer variability testing, 148/182 RFs showed robustness on T1-HBP, and 141/182 on T2 (ICC > 0.7).Cox multivariate analysis was run to establish clinical (CLIN-mod), radiomic (RAD-T1, RAD-T2), and combined (COMB-T1, COMB-T2) models for LTPFS prediction. RESULTS: Seventy-six CLMs (43 patients) were assessed. Median follow-up was 14 months. LTP occurred in 19 lesions (25%).CLIN-mod was composed of minimal ablation margins (MAMs), intra-segment progression and primary tumor grade and exhibited moderately high discriminatory power in predicting LTPFS (AUC = 0.89, p = 0.0001). Both RAD-T1 and RAD-T2 were able to predict LTPFS: (RAD-T1: AUC = 0.83, p = 0.0003; RAD-T2: AUC = 0.79, p = 0.001). Combined models yielded the strongest performance (COMB-T1: AUC = 0.98, p = 0.0001; COMB-T2: AUC = 0.95, p = 0.0003). Both combined models included MAMs and tumor regression grade; COMB-T1 also featured 10th percentile of signal intensity, while tumor flatness was present in COMB-T2. CONCLUSION: MRI-based radiomic evaluation of CLMs is feasible and potentially useful for LTP prediction. Combined models outperformed clinical or radiomic models alone for LTPFS prediction.