On the use of flexible excess hazard regression models for describing long-term breast cancer survival: a case-study using population-based cancer registry data

Long-term excess mortality and net survival among elderly diffuse large B-cell lymphoma patients after front-line R-CHOP treatment

In the era of immunochemotherapy, data on the long-term prognosis of elderly patients diagnosed with a diffuse large B-cell lymphoma (DLBCL) are scarce. In this population and on the longer term, other-cause mortality is an important competing risk that needs to be accounted for. Using clinical trial data and relative survival approaches, we estimated the 10-year net survival (NS) and we described the excess mortality hazard (EMH) due (directly or indirectly) to the DLBCL, over time and according to main prognosis factors using flexible regression modelling. The 10-year NS was 65% [59; 71]. Using the flexible modelling, we showed that the EMH decreases steeply after diagnosis. The variables 'performance status', 'number of extra-nodal sites' and the serum 'lactate dehydrogenase' were strongly associated with the EMH, even after adjustment on other important variables. EMH is very close to zero at 10 years for the whole population, so DLBCL patients do not experience an increased mortality compared to the general population in the long term. The number of extra-nodal sites was an important prognostic factor shortly after diagnosis, suggesting that it is correlated with an important but unmeasured prognostic factor that would lead to this selection effect over time.

Association Between Contrast-Enhanced Ultrasound Characteristics and Molecular Subtypes of Breast Cancer

OBJECTIVE

This study aimed to investigate the correlation between contrast-enhanced ultrasound (CEUS) features and molecular subtypes of breast cancer (BC).

METHODS

A total of 116 patients (116 lesions) with pathologically diagnosed BC who received conventional ultrasound and CEUS before surgery were enrolled in this study. BC molecular subtypes were identified by postoperative pathological and immunohistochemical analysis as Luminal A (LA), Luminal B (LB), HER2 (H2) over-expression, and triple-negative (TN). Qualitative and quantitative CEUS characteristics were analyzed by one-way analysis of variance (continuous variables) or Pearson's χ² test or Fisher's exact probability method (categorical variables).

RESULTS

There were significant differences in enhancement speed and enhancement degree among the four subtypes (P < .05). The area under the curve (AUC), time to peak (TTP), and peak intensity (PI) differed among the four subtypes (P < .05). The AUC of the LA subtype (305.1 ± 188.4) was significantly smaller compared with the H2 (535.7 ± 222.0, P = .007) and TN subtypes (496.6 ± 254.7, P = .019). In addition, TTP was shorter in the H2 subtype (19.8 ± 4.9) compared with the other subtypes, and was significantly shorter than in the LA subtype (26.3 ± 7.2, P = .008) and LB subtype (23.1 ± 6.7, P = .036). The PI of the LA subtype (4.7 ± 2.3) was significantly lower than that of the LB (6.6 ± 2.3, P = .027), H2 (7.4 ± 2.2, P = .005), and TN subtypes (6.9 ± 2.6, P = .014).

CONCLUSIONS

CEUS features differed significantly among different molecular subtypes of BC. The enhancement patterns and parameters may be important predictive features of different subtypes of BC.

Distinction of Students and Expert Therapists Based on Therapeutic Motions on a Robotic Device Using Support Vector Machine

Purpose

To clarify the feature values of exercise therapy that can differentiate students and expert therapists and use this information as a reference for exercise therapy education.

Methods

The participants were therapists with 5 or more years of clinical experience and 4th year students at occupational therapist training schools who had completed their clinical practices. The exercise therapy task included Samothrace (code name, SAMO) exercises implemented on the elbow joint based on the elbow flexion angle, angular velocity, and exercise interval recordings. For analyses and student/therapist comparisons, the peak flexion angle, peak velocity, and movement time were calculated using data on elbow angle changes acquired via SAMO. Subsequently, bootstrap data were generated to differentiate between the exercise therapy techniques adopted by therapists and students, and a support vector machine was used to generate four types of data combinations with the peak flexion angle, peak velocity, and movement time values. These data were used to estimate and compare the respective accuracies with the Friedman test.

Results

The peak flexion angles were significantly smaller in the case of students. Furthermore, the peak velocities were larger, the peak flexion angles were smaller, and the movement times were shorter compared with those of therapists. The combination of peak velocity and peak flexion angle yielded the highest diagnostic accuracies.

Conclusion

When students and therapists performed upper limb exercise therapy techniques based on the kinematics movement of a robot arm, the movement speeds and joint angles differed. The combination of peak velocity and peak flexion angle was the most effective classifier used for the differentiation of the abilities of students and therapists. The peak velocity and peak flexion angle of the therapist group can be used as a reference for students when they learn upper limb therapeutic exercise techniques.

Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

Purpose

Traditionally, clinical evaluation of motor paralysis following stroke has been of value to physicians and therapists because it allows for immediate pathophysiological assessment without the need for specialized tools. However, current clinical methods do not provide objective quantification of movement; therefore, they are of limited use to physicians and therapists when assessing responses to rehabilitation. The present study aimed to create a support vector machine (SVM)-based classifier to analyze and validate finger kinematics using the leap motion controller. Results were compared with those of 24 stroke patients assessed by therapists.

Methods

A non-linear SVM was used to classify data according to the Brunnstrom recovery stages of finger movements by focusing on peak angle and peak velocity patterns during finger flexion and extension. One thousand bootstrap data values were generated by randomly drawing a series of sample data from the actual normalized kinematics-related data. Bootstrap data values were randomly classified into training (940) and testing (60) datasets. After establishing an SVM classification model by training with the normalized kinematics-related parameters of peak angle and peak velocity, the testing dataset was assigned to predict classification of paralytic movements.

Results

High separation accuracy was obtained (mean 0.863; 95% confidence interval 0.857–0.869; p = 0.006).

Conclusion

This study highlights the ability of artificial intelligence to assist physicians and therapists evaluating hand movement recovery of stroke patients.