Is Postoperative Radiotherapy Needed in the Management of Adult Craniopharyngiomas?

BACKGROUND: The optimal treatment of adult craniopharyngioma (CP) remains controversial. Although benign, these tumors tend to recur locally. The choice between gross total resection (GTR) versus subtotal resection (STR) with adjuvant or delayed radiotherapy (RT) is debated. The objective of this study is to review our experience with adult CPs over a 20-year period and identify an optimal management strategy. METHODS: From 1999 to 2020, we reviewed all patients diagnosed with CP at our institution. We collected data regarding tumor characteristics, treatments, and toxicity. Disease progression was defined as growth on imaging. Descriptive statistics were used to assess patient characteristics. The Kaplan Meier method was used to assess progression-free survival (PFS) and corresponding 95% confidence intervals (CI) from the time since treatment initiation. RESULTS: Twenty-four patients with a median age of 50 were included in this study. The median follow-up was 85 months. Seven patients had initial GTR, 10 STR, and 7 STR + RT. The overall 5-year PFS was 56% (95% CI: 38-83%): 100% in the STR + RT group, 69% in the GTR group, and 18% in the STR group (p = 0.01). Of the 17 patients initially treated with surgery alone, 3 with GTR and 6 with STR required salvage RT at a median of 46 months, with no further progression after salvage RT. CONCLUSIONS: Our study underscores the importance of RT for local control and suggests that STR + RT should be considered a viable option in the management of these tumors as it may be associated with improved PFS compared to surgery alone.

The Clinical Significance of Bone Mineral Density Changes Following Long-Term Androgen Deprivation Therapy in Localized Prostate Cancer Patients.

PURPOSE: Long-term androgen deprivation therapy has been associated with decreased bone mineral density in men with prostate cancer. Some evidence suggests that there is no impact on fracture risk despite this bone mineral density loss. Our study aimed to quantify changes in bone mineral density in men with high risk prostate cancer on long-term androgen deprivation therapy and calcium and vitamin D supplementation. MATERIALS AND METHODS: Bone mineral density analysis was conducted for localized high risk prostate cancer patients enrolled in the phase III randomized trial PCS-V (Prostate Cancer Study 5), comparing conventional and hypofractionated radiation therapy. Patients received 28 months of luteinizing hormone-releasing hormone agonist and calcium and vitamin D supplementation (500 mg calcium BID+400 IU vitamin D3 BID). The areal density and T-scores (spine, femoral neck and total femur) at baseline and 30 months of followup were extracted, and the absolute change was calculated. Clinical bone density status (normal, osteopenia, osteoporosis) was monitored. RESULTS: The lumbar spine, femoral neck and total femoral bone mineral density were measured for 226, 231, and 173 patients, respectively. The mean percent change in bone mineral density was -2.65%, -2.76% and -4.27% for these respective sites (p <0.001 for all). The average decrease in bone mineral density across all sites was -3.2%, with no decline in bone mineral density category in most patients (83%). Eight patients (4%) became osteoporotic. CONCLUSIONS: Despite a mild decline in bone mineral density, the change in clinical bone mineral density category remained low with long-term androgen deprivation therapy. Consequently, calcium and vitamin D supplementation alone may suffice for most localized prostate cancer patients on long-term androgen deprivation therapy.

Development of a generalizable natural language processing pipeline to extract physician-reported pain from clinical reports: Generated using publicly-available datasets and tested on institutional clinical reports for cancer patients with bone metastases.

OBJECTIVE: The majority of cancer patients suffer from severe pain at the advanced stage of their illness. In most cases, cancer pain is underestimated by clinical staff and is not properly managed until it reaches a critical stage. Therefore, detecting and addressing cancer pain early can potentially improve the quality of life of cancer patients. The objective of this research project was to develop a generalizable Natural Language Processing (NLP) pipeline to find and classify physician-reported pain in the radiation oncology consultation notes of cancer patients with bone metastases. MATERIALS AND METHODS: The texts of 1249 publicly-available hospital discharge notes in the i2b2 database were used as a training and validation set. The MetaMap and NegEx algorithms were implemented for medical terms extraction. Sets of NLP rules were developed to score pain terms in each note. By averaging pain scores, each note was assigned to one of the three verbally-declared pain (VDP) labels, including no pain, pain, and no mention of pain. Without further training, the generalizability of our pipeline in scoring individual pain terms was tested independently using 30 hospital discharge notes from the MIMIC-III database and 30 consultation notes of cancer patients with bone metastasis from our institution's radiation oncology electronic health record. Finally, 150 notes from our institution were used to assess the pipeline's performance at assigning VDP. RESULTS: Our NLP pipeline successfully detected and quantified pain in the i2b2 summary notes with 93% overall precision and 92% overall recall. Testing on the MIMIC-III database achieved precision and recall of 91% and 86% respectively. The pipeline successfully detected pain with 89% precision and 82% recall on our institutional radiation oncology corpus. Finally, our pipeline assigned a VDP to each note in our institutional corpus with 84% and 82% precision and recall, respectively. CONCLUSION: Our NLP pipeline enables the detection and classification of physician-reported pain in our radiation oncology corpus. This portable and ready-to-use pipeline can be used to automatically extract and classify physician-reported pain from clinical notes where the pain is not otherwise documented through structured data entry.

Duration and cessation characteristics of heparinization after finger replantation: A retrospective analysis of outcomes.

BACKGROUND: To prevent postoperative thrombosis, indications for anticoagulation in finger replantation have been described, but no consensus has yet been found for cessation protocols. The aim of this study is to investigate cessation methods of intravenous anticoagulation after finger replantation. METHODS: A retrospective review of all patients treated for a finger replantation between December 2014 and July 2016 was performed. Only those who required postoperative treatment with intravenous heparin were extracted. Primary outcome was survival of finger at hospital discharge and data collection focused on postoperative anticoagulation regimens. RESULTS: 108 patients with replantation were treated with intravenous heparin and included in the analysis. When anticoagulated, survival rate was 60% (n = 65) at hospital discharge, wherein arterial and venous thrombosis accounted for 60 and 40% respectively. Descriptive analysis failed to demonstrate an increase in failure rates when tested for duration of intravenous heparin, fixed or variable infusion rates of anticoagulation and need for vascular grafts. However, there was a 2.8-fold (P = .009) increase in the survival rate with progressive weaning of anticoagulation rather than abrupt discontinuation. Subgroup analysis demonstrated similar findings when considering arterial thrombosis alone (OR 5.2, P = .012), but did not show any significant difference for venous thrombosis (OR 1.7, P = .344). CONCLUSIONS: Progressive tapering of intravenous heparin is associated with an increased survival rate after finger replantation, particularly for arterial thrombosis. Further prospective and randomized trials are necessary to elucidate the optimal duration, method of infusion and indications for vascular grafts.

A Scalable Radiomics- and Natural Language Processing-Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study.

BACKGROUND: The identification of objective pain biomarkers can contribute to an improved understanding of pain, as well as its prognosis and better management. Hence, it has the potential to improve the quality of life of patients with cancer. Artificial intelligence can aid in the extraction of objective pain biomarkers for patients with cancer with bone metastases (BMs). OBJECTIVE: This study aimed to develop and evaluate a scalable natural language processing (NLP)- and radiomics-based machine learning pipeline to differentiate between painless and painful BM lesions in simulation computed tomography (CT) images using imaging features (biomarkers) extracted from lesion center point-based regions of interest (ROIs). METHODS: Patients treated at our comprehensive cancer center who received palliative radiotherapy for thoracic spine BM between January 2016 and September 2019 were included in this retrospective study. Physician-reported pain scores were extracted automatically from radiation oncology consultation notes using an NLP pipeline. BM center points were manually pinpointed on CT images by radiation oncologists. Nested ROIs with various diameters were automatically delineated around these expert-identified BM center points, and radiomics features were extracted from each ROI. Synthetic Minority Oversampling Technique resampling, the Least Absolute Shrinkage And Selection Operator feature selection method, and various machine learning classifiers were evaluated using precision, recall, F1-score, and area under the receiver operating characteristic curve. RESULTS: Radiation therapy consultation notes and simulation CT images of 176 patients (mean age 66, SD 14 years; 95 males) with thoracic spine BM were included in this study. After BM center point identification, 107 radiomics features were extracted from each spherical ROI using pyradiomics. Data were divided into 70% and 30% training and hold-out test sets, respectively. In the test set, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of our best performing model (neural network classifier on an ensemble ROI) were 0.82 (132/163), 0.59 (16/27), 0.85 (116/136), and 0.83, respectively. CONCLUSIONS: Our NLP- and radiomics-based machine learning pipeline was successful in differentiating between painful and painless BM lesions. It is intrinsically scalable by using NLP to extract pain scores from clinical notes and by requiring only center points to identify BM lesions in CT images.

Stereotactic Ablative Radiation Therapy for the Treatment of Upper Urinary Tract Urothelial Carcinoma.

PURPOSE: Urothelial carcinomas (UCs), also known as transitional cell carcinomas, account for the majority of upper urinary tract tumors. The gold-standard therapy for operable patients with localized disease is radical nephroureterectomy. However, some patients are not surgical candidates. Data on the use of modern radiation therapy for upper urinary tract UC (UTUC) are scarce. The purpose of this study was to assess the safety and efficacy of SABR in UTUC. METHODS AND MATERIALS: This retrospective study included all patients with UTUC treated with SABR at one institution. Charts were reviewed to evaluate renal function and the development of toxicity using Common Terminology Criteria for Adverse Events, version 3.0. Tumor response on follow-up imaging with computed tomography or magnetic resonance imaging scans was assessed using the Response Evaluation Criteria in Solid Tumors, version 1.1. RESULTS: A total of 16 patients (7 patients with UC at the ureter and 9 at the renal pelvis) were identified as treated with SABR. Of the 9 patients with renal pelvis UC, 4 had a previous history of bladder cancer. At the time of treatment, the median age was 85 years (range, 67-95 years). Most patients received 40 Gy in 8 fractions every second day. The median followup was 21 months (range, 3-110 months). Most patients maintained stable renal function, and only 2 patients developed worsening chronic kidney disease, but none required dialysis. Acutely, 4 patients developed grade 1 diarrhea, and 1 patient had new grade 1 hematuria. No chronic side effects were observed. One patient did not have follow-up imaging and was excluded from the tumor-response analysis. Two patients had a complete response of the treated lesion, 9 had a partial response, 2 had stable disease, and 2 had disease progression within the treatment field. CONCLUSIONS: This small case series suggests that SABR for UTUC is safe and well-tolerated, with good radiographic tumor response to ablative doses of radiation therapy.

Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest.

Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is time consuming and oncologists often outline just RT treatment fields in clinical practice. This presents a challenge for real-world radiomics research. As such, a method that simplifies BM identification but does not compromise the power of radiomics is needed. The objective of this study was to investigate the feasibility of radiomics models for BM detection using lesion-center-based geometric ROIs. The planning-CT images of 170 patients with non-metastatic lung cancer and 189 patients with spinal BM were used. The point locations of 631 BM and 674 healthy bone (HB) regions were identified by experts. ROIs with various geometric shapes were centered and automatically delineated on the identified locations, and 107 radiomics features were extracted. Various feature selection methods and machine learning classifiers were evaluated. Our point-based radiomics pipeline was successful in differentiating BM from HB. Lesion-center-based segmentation approach greatly simplifies the process of preparing images for use in radiomics studies and avoids the bottleneck of full ROI segmentation.

Creation and Pilot-testing of Virtual Patients for Learning Oncologic Emergency Management.

Purpose or objective Management of oncologic emergencies becomes critical at the start of the second year of a radiation oncology residency. Considering the limited exposure to oncology in the medical school curriculum, this knowledge gap needs to be filled prior to managing real patients. The aim of this project was to create virtual patients (VPs) to ease this transition and improve learner readiness for independently managing oncologic emergencies on call. Material and methods A curriculum mapping exercise was done to identify gaps. The main oncologic emergencies that needed to be addressed were selected for development of the modules. Review of the key concepts for management was elucidated and validated. These included history, physical examination, imaging interpretation, staging, as well as anatomy, epidemiology, pertinent literature, differential diagnosis, prognostication, radiation treatment planning, summarizing, and patient- and peer-communication skills. Clinical vignettes were then designed, in collaboration with a virtual patient education expert, to mimic the clinical presentation and evolution of a typical patient for three common oncologic emergencies: spinal cord compression, superior vena cava syndrome, and tumor-induced hemorrhage. Results Three virtual modules were developed: spinal cord compression, superior vena cava syndrome, and tumor-induced hemorrhage. Each case included 25 to 30 vignettes that participants progressed through, with a total estimated completion time of 30 to 45 minutes. Each node branched out to provide a detailed answer and explanation of the key concept. Figures were included to mimic real patients and to provide a more authentic learning experience. The modules also included quantitative pre- and post-testing assessments, including multiple-choice questions, true or false, fill in the blank, short answers, and text response. The cases were then transcribed onto a virtual patient simulation platform. Following completion of the module, a report was generated for each individual learner to track all responses and used as the assessment tool. The pilot test showed an increase of 28% in the pre-to-post-test results in a cohort of nine residents. The mean pre-test result of 58% increased to a mean post-test result of 86% (range: 70-100%) after completing the three modules. Conclusion VPs can be used for learning the management of oncologic emergencies and can be done on a simulation-based learning platform. The modules can be used as both, a learning and an assessment tool for junior residents. The results of the pilot test show a significant improvement in knowledge acquisition between pre- and post-test scores after completion of the three modules.