Diffusion-weighted MR imaging in pancreatic ductal adenocarcinoma: prediction of next-generation sequencing-based tumor cellularity and prognosis after surgical resection

The minimum apparent diffusion coefficient value on preoperative magnetic resonance imaging in resectable pancreatic cancer: a new prognostic factor for biologically borderline resectable pancreatic cancer

PURPOSE

To identify the prognostic factors that can define biologically borderline resectable pancreatic cancer (BRPC) in resectable pancreatic cancer (RPC) patients.

METHODS

This retrospective study included 121 R/BRPC patients who underwent upfront surgery. Univariate and multivariate analyses were conducted to investigate the relationship between preoperative factors and overall survival (OS) for RPC. The OS of RPC patients was stratified based on a score, with each independent prognostic factor receiving 1 point. The OS of the R/BRPC patients was compared based on their scores.

RESULTS

Overall, 113 and eight patients had RPC and BRPC. Serum CA19-9 > 500 U/mL (p = 0.048), maximum tumor diameter > 30 mm (p = 0.01), superior mesenteric/portal vein contact < 180° (p = 0.04), and minimum apparent diffusion coefficient (ADCmin) ≤ 1020 × 10-6 mm2/s (p = 0.01) were identified as independent prognostic factors in RPC patients. RPC patients with a score of 0 had a significantly better prognosis than those with scores of 1 and 2-4 and BRPC patients (median OS: 99.3, 35.1, 19.0, and 8.4 months; p = 0.007, p < 0.001, and p = 0.003, respectively). No significant difference in the prognosis was observed between BRPC and RPC patients with scores of 1 and 2-4.

CONCLUSIONS

Preoperative ADCmin in RPC may be a new prognostic factor for biological BRPC.

Looking at or beyond the tumor - a systematic review and meta-analysis of quantitative imaging biomarkers predicting pancreatic cancer prognosis

OBJECTIVES

To evaluate the prognostic value of quantitative imaging biomarkers derived from computed tomography (CT) and magnetic resonance imaging (MRI) for pancreatic cancer (PC), with a particular focus on body composition parameters beyond the traditional intrinsic features of the tumor.

METHODS

PubMed, EMBASE, and Cochrane Library databases were searched for articles on quantitative imaging biomarkers obtained from CT or MRI in predicting PC prognosis published between January 2014 and August 2024. The Newcastle-Ottawa scale was used to assess the quality of the included studies. Survival outcomes, such as overall survival (OS) and recurrence-free survival (RFS), were evaluated. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using a random-effects model. In case of high heterogeneity, subgroup analyses and sensitivity analyses were performed to identify potential sources of heterogeneity among the studies.

RESULTS

We performed a meta-analysis of ten imaging biomarkers investigated in 43 included studies. Larger tumor size, lower skeletal muscle radiodensity, lower skeletal muscle index (SMI), presence of sarcopenic obesity, lower psoas muscle index (PMI), higher visceral to subcutaneous adipose tissue area ratio, and lower visceral adipose tissue index were associated with significantly worse OS. In particular, lower SMI and lower PMI had relatively high HRs (1.65 for SMI, 95% CI 1.39-1.96, and 2.20 for PMI, 95% CI 1.74-2.78). Patients with lower SMI exhibited poorer RFS (HR 1.78, 95% CI 1.46-2.18). Subgroup analyses identified the origin region of the study and intervention type as potential factors of heterogeneity for SMI in predicting OS.

CONCLUSIONS

Imaging biomarkers indicating body composition at PC diagnosis may play an important role in predicting patient prognosis. Further prospective multi-center studies with large sample sizes are needed for validation and translation into clinical practice.

Motion-Compensated Multishot Pancreatic Diffusion-Weighted Imaging With Deep Learning-Based Denoising

OBJECTIVES

Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal. Thus, a method that combines msDWI with MCGs while minimizing the echo time penalty and maximizing signal would improve pancreatic DWI. In this work, we combine MCGs generated via convex-optimized diffusion encoding (CODE), which reduces the echo time penalty of motion compensation, with deep learning (DL)-based denoising to address residual signal loss. We hypothesize this method will qualitatively and quantitatively improve msDWI of the pancreas.

MATERIALS AND METHODS

This prospective institutional review board-approved study included 22 patients who underwent abdominal MR examinations from August 22, 2022 and May 17, 2023 on 3.0 T scanners. Following informed consent, 2-shot spin-echo echo-planar DWI (b = 0, 800 s/mm2) without (M0) and with (M1) CODE-generated first-order gradient moment nulling was added to their clinical examinations. DL-based denoising was applied to the M1 images (M1 + DL) off-line. ADC maps were reconstructed for all 3 methods. Blinded pair-wise comparisons of b = 800 s/mm2 images were done by 3 subspecialist radiologists. Five metrics were compared: pancreatic boundary delineation, motion artifacts, signal homogeneity, perceived noise, and diagnostic preference. Regions of interest of the pancreatic head, body, and tail were drawn, and mean ADC values were computed. Repeated analysis of variance and post hoc pairwise t test with Bonferroni correction were used for comparing mean ADC values. Bland-Altman analysis compared mean ADC values. Reader preferences were tabulated and compared using Wilcoxon signed rank test with Bonferroni correction and Fleiss κ.

RESULTS

M1 was significantly preferred over M0 for perceived motion artifacts and signal homogeneity (P < 0.001). M0 was significantly preferred over M1 for perceived noise (P < 0.001), but DL-based denoising (M1 + DL) reversed this trend and was significantly favored over M0 (P < 0.001). ADC measurements from M0 varied between different regions of the pancreas (P = 0.001), whereas motion correction with M1 and M1 + DL resulted in homogeneous ADC values (P = 0.24), with values similar to those reported for ssDWI with motion correction. ADC values from M0 were significantly higher than M1 in the head (bias 16.6%; P < 0.0001), body (bias 11.0%; P < 0.0001), and tail (bias 8.6%; P = 0.001). A small but significant bias (2.6%) existed between ADC values from M1 and M1 + DL.

CONCLUSIONS

CODE-generated motion compensating gradients improves multishot pancreatic DWI as interpreted by expert readers and eliminated ADC variation throughout the pancreas. DL-based denoising mitigated signal losses from motion compensation while maintaining ADC consistency. Integrating both techniques could improve the accuracy and reliability of multishot pancreatic DWI.

Impact on the image quality of modified reduced field-of-view diffusion-weighted magnetic resonance imaging of pancreatic adenocarcinoma using spatially tailored two-dimensional radiofrequency pulses with a tilted excitation plane: A comparison with conventional field-of-view imaging

PURPOSE

Modified reduced FOV diffusion-weighted imaging (DWI) using spatially-tailored 2D RF pulses with tilted excitation plane (tilted r-DWI) has been developed. The purpose of this study was to evaluate the impact on image quality and quantitative apparent diffusion coefficient (ADC) values of tilted r-DWI for pancreatic ductal adenocarcinomas (PDAC) in comparison to conventional full-FOV DWI (f-DWI).

METHODS

This retrospective study included 21 patients (mean 70.7, range 50-85 years old) with pathologically confirmed PDAC. All MR images were obtained using 3 T systems. Two radiologists evaluated presence of blurring or ghost artifacts, susceptibility artifacts, and aliasing artifacts; anatomic visualization of the pancreas; interslice signal homogeneity; overall image quality; and conspicuity of the PDAC. For quantitative analysis, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), signal-intensity ratio (SIR) and ADC values were measured using regions of interest.

RESULTS

All image quality scores except aliasing artifacts in tilted r-DWI were significantly higher than those in f-DWI (p < 0.01). The CNR and SIR of PDAC were significantly higher in tilted r-DWI than in f-DWI (6.7 ± 4.4 vs. 4.7 ± 3.9, 2.02 ± 0.72 vs. 1.72 ± 0.60, p < 0.01). Conversely, the SNR of PDAC in tilted r-DWI was significantly lower than that in f-DWI (56.0 ± 33.1 vs. 113.6 ± 67.3, p < 0.01). No significant difference was observed between mean ADC values of the PDAC calculated from tilted r-DWI (tilted r-ADC) and those from f-DWI (f-ADC) (1225 ± 250 vs. 1294 ± 302, p = 0.11).

CONCLUSION

The r-DWI using 2D RF techniques with a tilted excitation plane was shown to significantly improve the image quality and CNR and reduce image artifacts compared to f-DWI techniques in MRI evaluations of PDAC without significantly affecting ADC values.

A review study on early detection of pancreatic ductal adenocarcinoma using artificial intelligence assisted diagnostic methods

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, chemo-refractory and recalcitrant cancer and increases the number of deaths. With just around 1 in 4 individuals having respectable tumours, PDAC is frequently discovered when it is in an advanced stage. Accordingly, ED of PDAC improves patient survival. Subsequently, this paper reviews the early detection of PDAC, initially, the work presented an overview of PDAC. Subsequently, it reviews the molecular biology of pancreatic cancer and the development of molecular biomarkers are represented. This article illustrates the importance of identifying PDCA, the Immune Microenvironment of Pancreatic Cancer. Consequently, in this review, traditional and non-traditional imaging techniques are elucidated, traditional and non-traditional methods like endoscopic ultrasound, Multidetector CT, CT texture analysis, PET-CT, magnetic resonance imaging, diffusion-weighted imaging, secondary signs of pancreatic cancer, and molecular imaging. The use of artificial intelligence in pancreatic cancer, novel MRI techniques, and the future directions of AI for PDAC detection and prognosis is then described. Additionally, the research problem definition and motivation, current trends and developments, state of art of survey, and objective of the research are demonstrated in the review. Consequently, this review concluded that Artificial Intelligence Assisted Diagnostic Methods with MRI images can be proposed in future to improve the specificity and the sensitivity of the work, and to classify malignant PDAC with greater accuracy.

Ultra-high b-Value DWI in predicting progression risk of locally advanced rectal cancer: a comparative study with routine DWI

BACKGROUND

The prognosis prediction of locally advanced rectal cancer (LARC) was important to individualized treatment, we aimed to investigate the performance of ultra-high b-value DWI (UHBV-DWI) in progression risk prediction of LARC and compare with routine DWI.

METHODS

This retrospective study collected patients with rectal cancer from 2016 to 2019. Routine DWI (b = 0, 1000 s/mm²) and UHBV-DWI (b = 0, 1700 ~ 3500 s/mm²) were processed with mono-exponential model to generate ADC and ADCuh, respectively. The performance of the ADCuh was compared with ADC in 3-year progression free survival (PFS) assessment using time-dependent ROC and Kaplan-Meier curve. Prognosis model was constructed with ADCuh, ADC and clinicopathologic factors using multivariate COX proportional hazard regression analysis. The prognosis model was assessed with time-dependent ROC, decision curve analysis (DCA) and calibration curve.

RESULTS

A total of 112 patients with LARC (TNM-stage II-III) were evaluated. ADCuh performed better than ADC for 3-year PFS assessment (AUC = 0.754 and 0.586, respectively). Multivariate COX analysis showed that ADCuh and ADC were independent factors for 3-year PFS (P < 0.05). Prognostic model 3 (TNM-stage + extramural venous invasion (EMVI) + ADCuh) was superior than model 2 (TNM-stage + EMVI + ADC) and model 1 (TNM-stage + EMVI) for 3-year PFS prediction (AUC = 0.805, 0.719 and 0.688, respectively). DCA showed that model 3 had higher net benefit than model 2 and model 1. Calibration curve demonstrated better agreement of model 1 than model 2 and model 1.

CONCLUSIONS

ADCuh from UHBV-DWI performed better than ADC from routine DWI in predicting prognosis of LARC. The model based on combination of ADCuh, TNM-stage and EMVI could help to indicate progression risk before treatment.

Prognostic value of multi b-value DWI in patients with locally advanced rectal cancer

OBJECTIVES

To evaluate the potential of multi b-value DWI in predicting the prognosis of patients with locally advanced rectal cancer (LARC).

METHODS

From 2015 to 2019, a total of 161 patients with LARC were enrolled and randomly sampled into a training set (n = 113) and validation set (n = 48). Multi b-value DWI (b = 0~1500 s/mm²) scans were postprocessed to generate functional parameters, including apparent diffusion coefficient (ADC), Dt, Dp, f, distributed diffusion coefficient (DDC), and α. Histogram features of each functional parameter were submitted into Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate COX analysis to generate DWI_score based on the training set. The prognostic model was constructed with functional parameter, DWI_score, and clinicopathologic factors by using univariate and multivariate COX analysis on the training set and verified on the validation set.

RESULTS

Multivariate COX analysis revealed that DWI_score was an independent indicator for 5-year progression-free survival (PFS, HR = 5.573, p < 0.001), but not for overall survival (OS, HR = 2.177, p = 0.051). No mean value of functional parameters was correlated with PFS or OS. Prognostic model for 5-year PFS based on DWI_score, TNM-stage, mesorectal fascia (MRF), and extramural venous invasion (EMVI) showed good performance both in the training set (AUC = 0.819) and validation set (AUC = 0.815).

CONCLUSIONS

The DWI_score based on histogram features of multi b-value DWI functional parameters was an independent factor for PFS of LARC and the prognostic model with a combination of DWI_score and clinicopathologic factors could indicate the progression risk before treatment.

KEY POINTS

• Mean value of functional parameters obtained from multi b-value DWI might not be useful to assess the prognosis of LARC. • The DWI_score based on histogram features of multi b-value DWI functional parameters was an independent prognosis factor for PFS of LARC. • Prognostic model based on DWI_score and clinicopathologic factors could indicate the progression risk of LARC before treatment.

Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions

The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic pseudocyst) to 34-68% (main duct intraductal papillary mucinous neoplasm). It is imperative to correctly risk-stratify the malignant potential of these lesions in order to provide the correct care course for the patient, ranging from monitoring to surgical intervention. Even with the multiplicity of guidelines (i.e., the American Gastroenterology Association guidelines and Fukuoka/International Consensus guidelines) and multitude of diagnostic information, risk stratification of PCLs falls short. Studies have reported that 25-64% of patients undergoing PCL resection have pancreatic cysts with no malignant potential, and up to 78% of mucin-producing cysts resected harbor no malignant potential on pathological evaluation. Clinicians are now incorporating artificial intelligence technology to aid in the management of these difficult lesions. This review article focuses on advancements in artificial intelligence within digital pathomics, radiomics, and genomics as they apply to the diagnosis and risk stratification of PCLs.