Optimization of treatment planning workflow and tumor coverage during daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT) of pancreatic cancer

Abstract Background To simplify the adaptive treatment planning workflow while achieving the optimal tumor-dose coverage in pancreatic cancer patients undergoing daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT). Methods In daily adaptive MR-IGRT, the plan objective function constructed during simulation is used for plan re-optimization throughout the course of treatment. In this study, we have constructed the initial objective functions using two methods for 16 pancreatic cancer patients treated with the ViewRay™ MR-IGRT system: 1) the conventional method that handles the stomach, duodenum, small bowel, and large bowel as separate organs at risk (OARs) and 2) the OAR grouping method. Using OAR grouping, a combined OAR structure that encompasses the portions of these four primary OARs within 3 cm of the planning target volume (PTV) is created. OAR grouping simulation plans were optimized such that the target coverage was comparable to the clinical simulation plan constructed in the conventional manner. In both cases, the initial objective function was then applied to each successive treatment fraction and the plan was re-optimized based on the patient’s daily anatomy. OAR grouping plans were compared to conventional plans at each fraction in terms of coverage of the PTV and the optimized PTV (PTV OPT), which is the result of the subtraction of overlapping OAR volumes with an additional margin from the PTV. Results Plan performance was enhanced across a majority of fractions using OAR grouping. The percentage of the volume of the PTV covered by 95% of the prescribed dose (D95) was improved by an average of 3.87 ± 4.29% while D95 coverage of the PTV OPT increased by 3.98 ± 4.97%. Finally, D100 coverage of the PTV demonstrated an average increase of 6.47 ± 7.16% and a maximum improvement of 20.19%. Conclusions In this study, our proposed OAR grouping plans generally outperformed conventional plans, especially when the conventional simulation plan favored or disregarded an OAR through the assignment of distinct weighting parameters relative to the other critical structures. OAR grouping simplifies the MR-IGRT adaptive treatment planning workflow at simulation while demonstrating improved coverage compared to delivered pancreatic cancer treatment plans in daily adaptive radiation therapy

A machine-learning approach to predict postprandial hypoglycemia

Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.11Ysciescopu

Nodular Fasciitis with Cortical Erosion of the Hand

Nodular fasciitis is a benign, reactive myofibroblastic tumor that is often mistaken for a sarcoma because of its histological appearance and rapid growth. Involvement of a finger is extremely rare. We report a case of nodular fasciitis of the thumb, accompanied by bone erosion. Magnetic resonance findings suggested the possibility of a malignancy, which could have led to misdiagnosis as a malignant soft tissue sarcoma. Instead, the lesion was treated by excisional biopsy, which confirmed nodular fasciitis. There has been no evidence of local recurrence at recent follow-up, 1 year after surgery. This case illustrates that, to avoid unnecessarily aggressive surgery, nodular fasciitis must be included in the differential diagnosis for any finger lesion that resembles a sarcoma, even if bone erosion is present

An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) in home healthcare is proposed. The whole system consists of two-lead electrocardiogram acquisition using conductive textile electrodes located in bed, baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problems of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we are proposing a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 male subjects, and we obtained satisfactory respiration signals that showed high correlation (r2 > 0.8) with the signal acquired from the chest-belt respiration sensor

Prediction of renal recovery following sepsis-associated acute kidney injury requiring renal replacement therapy using contrast-enhanced ultrasonography

Background Microcirculatory dysfunction plays a critical role in sepsis-associated acute kidney injury (S-AKI) development; however, its impact on renal recovery remains uncertain. We investigated the association between cortical microcirculatory function assessed using contrast-enhanced ultrasonography (CEUS) and renal recovery after S-AKI needing renal replacement therapy (RRT). Methods This retrospective study included 23 patients who underwent CEUS among those who underwent acute RRT for S-AKI. In addition, we acquired data from 17 healthy individuals and 18 patients with chronic kidney disease. Renal recovery was defined as sustained independence from RRT for at least 14 days. Results Of the CEUS-derived parameters, rise time, time to peak, and fall time were longer in patients with S-AKI than in healthy individuals (p = 0.045, 0.01, and 0.096, respectively). Fourteen patients (60.9%) with S-AKI receiving RRT experienced renal recovery; and these patients had higher values of peak enhancement, wash-in area under the curve (AUC), wash-in perfusion index, and wash-out AUC than those without recovery (p = 0.03, 0.01, 0.03, and 0.046, respectively). We evaluated the receiver operating characteristic curve and found that the peak enhancement, wash-in AUC, wash-in perfusion index, and wash-out AUC of CEUS derivatives estimated the probability of renal recovery after S-AKI requiring RRT (p = 0.03, 0.01, 0.03, and 0.04, respectively). Conclusion CEUS-assessed cortical microvascular perfusion may predict renal recovery following S-AKI that requires RRT. Further studies are essential to validate the clinical utility of microcirculatory parameters obtained from CEUS to estimate renal outcomes in various etiologies and severities of kidney disease