Improving referral rates for smoking cessation:A multifaceted intervention aimed at radiation oncologists

Objective: Continuation of smoking after a cancer diagnosis increases the burdensome side effects from cancer treatment, and decreases the chances of cure. Smoking cessation may improve oncological outcomes in cancer patients. This study aims to evaluate if radiation oncologists can be motivated by a smoking cessation awareness intervention to discuss smoking status more frequently and increase the referral rate for smoking cessation-support. Study design and Setting: A multifaceted approach was used to improve awareness: First, current practice was evaluated by a retrospective chart review of 282 patients referred for radiotherapy to ascertain smoking status, discussion of smoking cessation support and referral rates. Secondly, radiation oncologists were interviewed about their motives and barriers to discuss smoking status and smoking cessation support. The results were fed back in a teaching lecture to the doctors involved. Finally, the effect of this smoking cessation awareness intervention was prospectively evaluated in 100 patients. Results: After the smoking cessation awareness intervention, smoking cessation was more frequently discussed compared to baseline (77% (10/13)) and 39.5% (17/43) respectively. The referral rate for smoking cessation increased from 2.3% (1/43) to 69.2% (9/13) Conclusion: Without an active smoking prevention awareness policy, referral for smoking cessation support for cancer patients by radiation oncologists is low. A relatively short and simple smoking awareness intervention for radiation oncologist may result in a more frequent discussion with patients about smoking cessation and an even larger increase in referrals for smoking cessation support.</p

Clinically acceptable segmentation of Organs at Risk in cervical cancer radiation treatment from clinically available annotations

Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical cancer radiation treatment from a large clinically available dataset of Computed Tomography (CT) scans containing data inhomogeneity, label noise, and missing annotations. We employ simple heuristics for automatic data cleaning to minimize data inhomogeneity and label noise. Further, we develop a semi-supervised learning approach utilizing a teacher-student setup, annotation imputation, and uncertainty-guided training to learn in presence of missing annotations. Our experimental results show that learning from a large dataset with our approach yields a significant improvement in the test performance despite missing annotations in the data. Further, the contours generated from the segmentation masks predicted by our model are found to be equally clinically acceptable as manually generated contours

Improving referral rates for smoking cessation: A multifaceted intervention aimed at radiation oncologists

Objective: Continuation of smoking after a cancer diagnosis increases the burdensome side effects from cancer treatment, and decreases the chances of cure. Smoking cessation may improve oncological outcomes in cancer patients. This study aims to evaluate if radiation oncologists can be motivated by a smoking cessation awareness intervention to discuss smoking status more frequently and increase the referral rate for smoking cessation-support.Study design and Setting:A multifaceted approach was used to improve awareness: First, current practice was evaluated by a retrospective chart review of 282 patients referred for radiotherapy to ascertain smoking status, discussion of smoking cessation support and referral rates. Secondly, radiation oncologists were interviewed about their motives and barriers to discuss smoking status and smoking cessation support. The results were fed back in a teaching lecture to the doctors involved. Finally, the effect of this smoking cessation awareness intervention was prospectively evaluated in 100 patients. Results: After the smoking cessation awareness intervention, smoking cessation was more frequently discussed compared to baseline (77% (10/13)) and 39.5% (17/43) respectively. The referral rate for smoking cessation increased from 2.3% (1/43) to 69.2% (9/13) Conclusion: Without an active smoking prevention awareness policy, referral for smoking cessation support for cancer patients by radiation oncologists is low. A relatively short and simple smoking awareness intervention for radiation oncologist may result in a more frequent discussion with patients about smoking cessation and an even larger increase in referrals for smoking cessation support

The role of hyperthermia in the treatment of locally advanced cervical cancer: a comprehensive review

Radiotherapy with cisplatin (chemoradiation) is the standard treatment for women with locally advanced cervical cancer. Radiotherapy with deep hyperthermia (thermoradiation) is a well established alternative, but is rarely offered as an alternative to chemoradiation, particularly for patients in whom cisplatin is contraindicated. The scope of this review is to provide an overview of the biological rationale of hyperthermia treatment delivery, including patient workflow, and the clinical effectiveness of hyperthermia as a radiosensitizer in the treatment of cervical cancer. Hyperthermia is especially effective in hypoxic and nutrient deprived areas of the tumor where radiotherapy is less effective. Its radiosensitizing effectiveness depends on the temperature level, duration of treatment, and the time interval between radiotherapy and hyperthermia. High quality hyperthermia treatment requires an experienced team, adequate online adaptive treatment planning, and is preferably performed using a phased array radiative locoregional hyperthermia device to achieve the optimal thermal dose effect. Hyperthermia is well tolerated and generally leads to only mild toxicity, such as patient discomfort. Patients in whom cisplatin is contraindicated should therefore be referred to a hyperthermia center for thermoradiation

Automatic landmark correspondence detection in medical images with an application to deformable image registration

Purpose: Deformable image registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images. Approach: We present a deep convolutional neural network (DCNN), called DCNN-Match, that learns to predict landmark correspondences in 3D images in a self-supervised manner. We trained DCNN-Match on pairs of computed tomography (CT) scans containing simulated deformations. We explored five variants of DCNN-Match that use different loss functions and assessed their effect on the spatial density of predicted landmarks and the associated matching errors. We also tested DCNN-Match variants in combination with the open-source registration software Elastix to assess the impact of predicted landmarks in providing additional guidance to DIR. Results: We tested our approach on lower abdominal CT scans from cervical cancer patients: 121 pairs containing simulated deformations and 11 pairs demonstrating clinical deformations. The results showed significant improvement in DIR performance when landmark correspondences predicted by DCNN-Match were used in the case of simulated (p = 0e0) as well as clinical deformations (p = 0.030). We also observed that the spatial density of the automatic landmarks with respect to the underlying deformation affect the extent of improvement in DIR. Finally, DCNN-Match was found to generalize to magnetic resonance imaging scans without requiring retraining, indicating easy applicability to other datasets. Conclusions: DCNN-match learns to predict landmark correspondences in 3D medical images in a self-supervised manner, which can improve DIR performance

Mutations in the human BOULE gene are not a major cause of impaired spermatogenesis

Mutation screening of the BOULE gene in 156 men with azoospermia or severe oligozoospermia revealed no relevant mutations; thus, mutations in BOULE can be eliminated as a major cause of impaired spemiatogenesis. (C)2005 by American Society for Reproductive Medicin

Learning clinically acceptable segmentation of Organs at Risk in cervical cancer radiation treatment from clinically available annotations

Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical cancer radiation treatment from a large clinically available dataset of Computed Tomography (CT) scans containing data inhomogeneity, label noise, and missing annotations. We employ simple heuristics for automatic data cleaning to minimize data inhomogeneity and label noise. Further, we develop a semi-supervised learning approach utilizing a teacher-student setup, annotation imputation, and uncertainty-guided training to learn in presence of missing annotations. Our experimental results show that learning from a large dataset with our approach yields a significant improvement in the test performance despite missing annotations in the data. Further, the contours generated from the segmentation masks predicted by our model are found to be equally clinically acceptable as manually generated contours

Analysis of isolated loco-regional recurrence rate in intermediate risk early cervical cancer after a type C2 radical hysterectomy without adjuvant radiotherapy

Background The efficacy of adjuvant radiotherapy in patients with intermediate risk early cervical cancer after radical hysterectomy is still under debate. Most guidelines recommend adjuvant radiotherapy, whereas others consider observation a viable option. Objective To investigate if patients with intermediate risk factors for cervical cancer who underwent radical hysterectomy may benefit from adjuvant radiotherapy. Methods Consecutive patients with tumor confined to the cervix and intermediate risk factors (according to Sedlis), treated between January 1982 and December 2014 who were observed after a type C2 radical hysterectomy formed the basis for this study. The frequency of recurrences, specifically isolated loco-regional recurrences, and the risk of death from recurrences, were analyzed. Data were analyzed using SPSS, version 23.0 for Windows Results A total of 161 patients were included in the analysis. Median age was 40 (range 20-76). Stages IB1 and IB2 were seen in 87 (54%) and 74 patients (46%), respectively. Squamous cell and non-squamous histology was seen in 114 (70.8%) and 47 patients (29.2%), respectively. Of the 161 patients, 25 (15.5%) had recurrent disease, of whom nine had an isolated loco-regional recurrence (5.6%). Median time to recurrence for isolated loco-regional recurrences was 28 months (range 9-151). Treatment for an isolated loco-regional recurrence was radiotherapy (n = 4) and chemoradiotherapy (n = 5). Four patients (2.5%) died from disease as a result of an isolated loco-regional recurrence. Actuarial disease-specific survival was 93.0% for the total group. No variables were found that predicted an isolated loco-regional recurrence. Discussion The mortality from isolated loco-regional recurrence in patients with intermediate risk factors for cervical cancer who underwent only radical hysterectomy type C2 was 2.5%. Further studies should compare outcomes between patients who undergo a type C2 radical hysterectomy without adjuvant radiotherapy with those undergoing a less radical hysterectomy but with adjuvant radiotherapy