Radiation therapy in the definitive management of medically inoperable endometrial cancer.

The incidence of endometrial cancer continues to increase worldwide with growing life expectancy and rates of obesity. While endometrial cancer is primarily a surgical disease managed with hysterectomy, a small proportion of patients are deemed to be poor surgical candidates due to their co-morbidities. These medically inoperable patients should be considered for curative treatment with definitive radiation therapy, and brachytherapy is an integral component of their care. Referral to a high-volume center early on in the care of potentially inoperable patients is crucial to optimize their management. These patients should be evaluated by a high-risk surgical and anesthesia team to confirm their medical inoperability. For inoperable patients, use of image-guided brachytherapy is encouraged. Brachytherapy applicator selection is determined based on a patient's anatomy, uterine size, and extent of tumor. Advances in anatomic and functional imaging including multiparametric magnetic resonance imaging (MRI) have improved clinical staging of these patients and have also allowed for the delivery of three-dimensional image-guided brachytherapy with improved accuracy. With recent consensus guidelines to guide local computed tomography and/or MRI volume-based delineation of targets and organs-at-risk, local outcomes have improved and treatments are delivered with less acute and late morbidity. Ongoing trials are looking at novel systemic agents, such as immunotherapy, to induce a systemic anti-tumor immune response and improve outcomes in these patients.

Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data.

OBJECTIVE. The purpose of this study was to test the hypothesis that convolutional neural networks can be used to predict which patients with pure atypical ductal hyperplasia (ADH) may be safely monitored rather than undergo surgery. MATERIALS AND METHODS. A total of 298 unique images from 149 patients were used for our convolutional neural network algorithm. A total of 134 images from 67 patients with ADH that had been diagnosed by stereotactic-guided biopsy of calcifications but had not been upgraded to ductal carcinoma in situ or invasive cancer at the time of surgical excision. A total of 164 images from 82 patients with mammographic calcifications indicated that ductal carcinoma in situ was the final diagnosis. Two standard mammographic magnification views of the calcifications (a craniocaudal view and a mediolateral or lateromedial view) were used for analysis. Calcifications were segmented using an open-source software platform and images were resized to fit a bounding box of 128 × 128 pixels. A topology with 15 hidden layers was used to implement the convolutional neural network. The network architecture contained five residual layers and dropout of 0.25 after each convolution. Patients were randomly separated into a training-and-validation set (80% of patients) and a test set (20% of patients). Code was implemented using open-source software on a workstation with an open-source operating system and a graphics card. RESULTS. The AUC value was 0.86 (95% CI, ± 0.03) for the test set. Aggregate sensitivity and specificity were 84.6% (95% CI, ± 4.0%) and 88.2% (95% CI, ± 3.0%), respectively. Diagnostic accuracy was 86.7% (95% CI, ± 2.9). CONCLUSION. It is feasible to apply convolutional neural networks to distinguish pure atypical ductal hyperplasia from ductal carcinoma in situ with the use of mammographic images. A larger dataset will likely result in further improvement of our prediction model.

Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset.

We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor dataset prior to initiation of chemotherapy. An institutional review board-approved retrospective review of our database from January 2009 to June 2016 identified 141 locally advanced breast cancer patients who (1) underwent breast MRI prior to the initiation of NAC, (2) successfully completed adriamycin/taxane-based NAC, and (3) underwent surgical resection with available final surgical pathology data. Patients were classified into three groups based on their NAC response confirmed on final surgical pathology: complete (group 1), partial (group 2), and no response/progression (group 3). A total of 3107 volumetric slices of 141 tumors were evaluated. Breast tumor was identified on first T1 postcontrast dynamic images and underwent 3D segmentation. CNN consisted of ten convolutional layers, four max-pooling layers, and dropout of 50% after a fully connected layer. Dropout, augmentation, and L2 regularization were implemented to prevent overfitting of data. Non-linear functions were modeled by a rectified linear unit (ReLU). Batch normalization was used between the convolutional and ReLU layers to limit drift of layer activations during training. A three-class neoadjuvant prediction model was evaluated (group 1, group 2, or group 3). The CNN achieved an overall accuracy of 88% in three-class prediction of neoadjuvant treatment response. Three-class prediction discriminating one group from the other two was analyzed. Group 1 had a specificity of 95.1% ± 3.1%, sensitivity of 73.9% ± 4.5%, and accuracy of 87.7% ± 0.6%. Group 2 (partial response) had a specificity of 91.6% ± 1.3%, sensitivity of 82.4% ± 2.7%, and accuracy of 87.7% ± 0.6%. Group 3 (no response/progression) had a specificity of 93.4% ± 2.9%, sensitivity of 76.8% ± 5.7%, and accuracy of 87.8% ± 0.6%. It is feasible for current deep CNN architectures to be trained to predict NAC treatment response using a breast MRI dataset obtained prior to initiation of chemotherapy. Larger dataset will likely improve our prediction model.

Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm.

OBJECTIVES: In the postneoadjuvant chemotherapy (NAC) setting, conventional radiographic complete response (rCR) is a poor predictor of pathologic complete response (pCR) of the axilla. We developed a convolutional neural network (CNN) algorithm to better predict post-NAC axillary response using a breast MRI dataset. METHODS: An institutional review board-approved retrospective study from January 2009 to June 2016 identified 127 breast cancer patients who: (1) underwent breast MRI before the initiation of NAC; (2) successfully completed Adriamycin/Taxane-based NAC; and (3) underwent surgery, including sentinel lymph node evaluation/axillary lymph node dissection with final surgical pathology data. Patients were classified into pathologic complete response (pCR) of the axilla group and non-pCR group based on surgical pathology. Breast MRI performed before NAC was used. Tumor was identified on first T1 postcontrast images underwent 3D segmentation. A total of 2811 volumetric slices of 127 tumors were evaluated. CNN consisted of 10 convolutional layers, 4 max-pooling layers. Dropout, augmentation and L2 regularization were implemented to prevent overfitting of data. RESULTS: On final surgical pathology, 38.6% (49/127) of the patients achieved pCR of the axilla (group 1), and 61.4% (78/127) of the patients did not with residual metastasis detected (group 2). For predicting axillary pCR, our CNN algorithm achieved an overall accuracy of 83% (95% confidence interval [CI] ± 5) with sensitivity of 93% (95% CI ± 6) and specificity of 77% (95% CI ± 4). Area under the ROC curve (0.93, 95% CI ± 0.04). CONCLUSIONS: It is feasible to use CNN architecture to predict post NAC axillary pCR. Larger data set will likely improve our prediction model.

Comparing preoperative imaging modalities in patient selection for breast intraoperative radiotherapy.

BACKGROUND: This study evaluated the relative accuracy of mammography, ultrasound, and magnetic resonance imaging (MRI) in predicting the tumor size of early stage breast tumors in preoperative selection of patients for intraoperative radiotherapy (IORT). METHODS: We identified 156 patients with clinical T1/T2, N0 breast cancer who underwent IORT. Clinical, pathologic, and radiation data were collected. The preoperative tumor size obtained by imaging was compared with tumor pathological size. RESULTS: The median patient age was 66. The mean tumor size at excision was 1.05 cm (0.1-3.0 cm). Out of the 156 patients, 98 had a reported, nonzero tumor size by mammography, 131 by ultrasound, and 76 by MRI. The mean difference between imaging and the tumor size was +0.062 ± 0.54 cm for mammography, -0.11 ± 0.43 cm for ultrasound, and +0.33 ± 0.55 cm for MRI, with positive values indicating an overestimate of the tumor size. MRI produced more overestimates of tumor size of at least 0.5 cm than mammography or ultrasound in a paired analysis of patients who received both modalities. CONCLUSIONS: Accuracy of imaging modalities in determining tumor size can influence patients' eligibility for IORT. Mammography and ultrasound showed acceptable accuracy in predicting size. MRI overestimated tumor size and may inappropriately exclude patients from IORT. We would discourage ruling out candidates for IORT on the basis of large size by MRI alone.

Dicer1 functions as a haploinsufficient tumor suppressor.

While the global down-regulation of microRNAs (miRNAs) is a common feature of human tumors, its genetic basis is largely undefined. To explore this question, we analyzed the consequences of conditional Dicer1 mutation (Dicer1 "floxed" or Dicer1(fl)) on several mouse models of cancer. Here we show Dicer1 functions as a haploinsufficient tumor suppressor gene. Deletion of a single copy of Dicer1 in tumors from Dicer1(fl/+) animals led to reduced survival compared with controls. These tumors exhibited impaired miRNA processing but failed to lose the wild-type Dicer1 allele. Moreover, tumors from Dicer1(fl/fl) animals always maintained one functional Dicer1 allele. Consistent with selection against full loss of Dicer1 expression, enforced Dicer1 deletion caused inhibition of tumorigenesis. Analysis of human cancer genome copy number data reveals frequent deletion of DICER1. Importantly, however, the gene has not been reported to undergo homozygous deletion, suggesting that DICER1 is haploinsufficient in human cancer. These findings suggest Dicer1 may be an important haploinsufficient tumor suppressor gene and, furthermore, that other factors controlling miRNA biogenesis may also function in this manner.

Engineering selection stringency on expression vector for the production of recombinant human alpha1-antitrypsin using Chinese Hamster ovary cells.

BACKGROUND: Expression vector engineering technology is one of the most convenient and timely method for cell line development to meet the rising demand of novel production cell line with high productivity. Destabilization of dihydrofolate reductase (dhfr) selection marker by addition of AU-rich elements and murine ornithine decarboxylase PEST region was previously shown to improve the specific productivities of recombinant human interferon gamma in CHO-DG44 cells. In this study, we evaluated novel combinations of engineered motifs for further selection marker attenuation to improve recombinant human alpha-1-antitrypsin (rhA1AT) production. Motifs tested include tandem PEST elements to promote protein degradation, internal ribosome entry site (IRES) mutations to impede translation initiation, and codon-deoptimized dhfr selection marker to reduce translation efficiency. RESULTS: After a 2-step methotrexate (MTX) amplification to 50 nM that took less than 3 months, the expression vector with IRES point mutation and dhfr-PEST gave a maximum titer of 1.05 g/l with the top producer cell pool. Further MTX amplification to 300 nM MTX gave a maximum titer of 1.15 g/l. Relative transcript copy numbers and dhfr protein expression in the cell pools were also analysed to demonstrate that the transcription of rhA1AT and dhfr genes were correlated due to the IRES linkage, and that the strategies of further attenuating dhfr protein expression with the use of a mutated IRES and tandem PEST, but not codon deoptimization, were effective in reducing dhfr protein levels in suspension serum free culture. CONCLUSIONS: Novel combinations of engineered motifs for further selection marker attenuation were studied to result in the highest reported recombinant protein titer to our knowledge in shake flask batch culture of stable mammalian cell pools at 1.15 g/l, highlighting applicability of expression vector optimization in generating high producing stable cells essential for recombinant protein therapeutics production. Our results also suggest that codon usage of the selection marker should be considered for applications that may involve gene amplification and serum free suspension culture, since the overall codon usage and thus the general expression and regulation of host cell proteins may be affected in the surviving cells.

Brachytherapy impacts on sexual function: An integrative review of the literature focusing on cervical cancer.

There is growing awareness of the importance of sexual health in the quality of life of cancer patients and survivors. Brachytherapy, a vital component for the curative treatment of cervical cancer, leads to both direct and indirect sequelae that result in vaginal and sexual morbidity. The emergence of 3D image-guided adaptive brachytherapy has led to a better understanding of dose-and-effect relationships for critical organs-at-risk and there are new recommendations for vaginal dose reporting in the ongoing EMBRACE II study. An understanding of the vagina as an organ-at-risk and its dose-and-effect relationships can help brachytherapists limit dose to the vagina and improve sexual morbidity. Brachytherapists play a critical role in the primary and secondary prevention of vaginal and sexual sequelae resulting from treatment. Through close surveillance and recognition of common symptoms, brachytherapists can intervene with effective strategies to prevent and treat vaginal and sexual symptoms. This review summarizes the current literature on dosimetric factors that may predict for vaginal morbidity. It will focus on quantitative and qualitative reports of brachytherapy-related vaginal toxicity and sexual dysfunction. Lastly, it will review the available evidence supporting clinical interventions to mitigate the development and progression of vaginal and sexual sequelae to improve functional quality post-treatment.

Adaptive Radiation Therapy: A Review of CT-based Techniques.

Adaptive radiation therapy is a feedback process by which imaging information acquired over the course of treatment, such as changes in patient anatomy, can be used to reoptimize the treatment plan, with the end goal of improving target coverage and reducing treatment toxicity. This review describes different types of adaptive radiation therapy and their clinical implementation with a focus on CT-guided online adaptive radiation therapy. Depending on local anatomic changes and clinical context, different anatomic sites and/or disease stages and presentations benefit from different adaptation strategies. Online adaptive radiation therapy, where images acquired in-room before each fraction are used to adjust the treatment plan while the patient remains on the treatment table, has emerged to address unpredictable anatomic changes between treatment fractions. Online treatment adaptation places unique pressures on the radiation therapy workflow, requiring high-quality daily imaging and rapid recontouring, replanning, plan review, and quality assurance. Generating a new plan with every fraction is resource intensive and time sensitive, emphasizing the need for workflow efficiency and clinical resource allocation. Cone-beam CT is widely used for image-guided radiation therapy, so implementing cone-beam CT-guided online adaptive radiation therapy can be easily integrated into the radiation therapy workflow and potentially allow for rapid imaging and replanning. The major challenge of this approach is the reduced image quality due to poor resolution, scatter, and artifacts. Keywords: Adaptive Radiation Therapy, Cone-Beam CT, Organs at Risk, Oncology © RSNA, 2023.