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Breast Cancer Outcomes and Toxicity Reduction with SGRT
Published in Jeremy D. P. Hoisak, Adam B. Paxton, Benjamin Waghorn, Todd Pawlicki, Surface Guided Radiation Therapy, 2020
Orit Kaidar-Person, Icro Meattini, Marianne C. Aznar, Philip Poortmans
Factors that were suggested by some to contribute to lung toxicity associated with RT include smoking, chemotherapy, tamoxifen, older age, preexisting lung disease, low lung capacity, and low performance status.39,41–43 RT-related factors that contribute to toxicity are the lung volume exposed and RT dose. The lung volume included in the RT field should be considered at the time of treatment planning to minimize potential toxicity.41 It is important to keep in mind that the lung doses are increased with more extensive regions irradiated: each nodal region adds about 3 Gy of ipsilateral mean lung dose (MLD).18 Importantly, the larger the volume irradiated, the more potential exists for lung dose reduction using DIBH.18 It is estimated that compared to free breathing, the ipsilateral MLD can be decreased by 1 Gy for breast or chest wall only; by 2 Gy for breast or chest wall and supraclavicular nodal fields and by 3 Gy for breast or chest wall, supraclavicular and IMN fields.18 These reductions do not take into account patients with a unique body habitus.
Long-Term Outcomes and Prognostic Factors in Patients with Indications for Particle Therapy in Sarcomas
Published in Manjit Dosanjh, Jacques Bernier, Advances in Particle Therapy, 2018
Beate Timmermann, Stephanie E. Combs
For patients with unresectable spinal osteosarcomas, a five-year LC, OS and PFS rate after carbon ion irradiation of 79%, 52% and 48%, respectively, was reported [74]. Patients with carbon irradiation doses of <64 gray-equivalent showed significantly more recurrences than those who received ≥64 GyE. A tumour volume ≤100 cubic centimetres and a vertical tumour size larger than 40 millimetres do significantly tend to show more local recurrences than tumours with volumes >100 cubic centimetres and ≥40 millimetres. Lower survival and tumour control rates are seen for unresectable osteosarcomas of the trunk treated with CIRT. Five-year OS, disease specific survival (DSS), PFS and LC rate were 33%, 34%, 23% and 62%, respectively [75]. Eastern Cooperative Oncology Group (ECOG) performance status of 1, CTV <500 cubic centimetres, normal alkaline phosphatase (ALP) and C-reactive protein (CRP) level were detected as significant prognostic factors positively influencing OS. LC was significantly superior in patients with performance status of 1 as well as with smaller clinical target volumes (CTV) (<500 vs. ≥500 cubic centimetres). Five-year OS and LC for unresectable osteosarcoma of the head and neck were 44.4% and 85.7%, respectively [76]. The results for the entire cohort demonstrated a significant difference in survival for gross tumour volume (GTV) (≥100 millilitres vs. <100 millilitres). LC was significantly higher for patients after irradiation with 70.4 GyE compared to lower total doses.
Will Systems Biology Transform Clinical Decision Support?
Published in Paul Cerrato, John Halamka, Reinventing Clinical Decision Support, 2020
The same scoring system inadequacies exist in oncology. One especially challenging area is assessing the likelihood of developing venous thrombo-embolism (VTE) in patients about to undergo chemotherapy. The Khorana score is currently being used to predict which patients are at greatest risk for VTE. It takes into account the type of cancer, the patient’s platelet count before chemotherapy, hemoglobin levels, pre-chemotherapy leukocyte count, and BMI. Unfortunately, more than half of all cancer patients fall into the intermediate risk category using the Khorana score—not a very sensitive or specific metric upon which to base treatment decisions. Patrrizia Ferroni, at the San Raffaele Roma Open University, in Rome, Italy, and her associates have devised a ML-based assessment system that improves risk stratification.14 Using kernel ML and random optimization (RO), they analyzed data from over 800 cancer patients to develop ML-RO predictors, which were then tested prospectively on 608 patients. Once again, the researchers took a broader approach to risk assessment, not limiting themselves to the variables included in the Khorana score. They included “age, sex, tumor site and stage, hematological attributes (including blood cell counts, hemoglobin, and neutrophil and platelet–lymphocyte ratios), fasting blood lipids, glycemic indexes, liver and kidney function, body mass index (BMI), Eastern Cooperative Oncology Group Performance Status (ECOG-PS), and supportive and anticancer drugs.”14 Their results, illustrated in Figure 6.4, demonstrated that ML-fueled risk analysis can outperform more traditional techniques.
Synergistic polymorphic interactions of phase II metabolizing genes and their association toward lung cancer susceptibility in North Indians
Published in International Journal of Environmental Health Research, 2022
Harleen Kaur Walia, Parul Sharma, Navneet Singh, Siddharth Sharma
Patients meeting all the following requirements shall be eligible for enrollment (i) Diagnosis of lung cancer (NSCLC or SCLC) is confirmed either by histology or cytology. (ii) stage III or IV disease. (iii) No age, gender, smoking, histology, and staging restrictions were applied. (iv) Untreated and intent to treat with definitive chemotherapy (Treated with platinum agents cisplatin/carboplatin, either as the first or second line). (v) An Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 0–2. (vi) At least one bi-dimensionally measurable lesion, according to the RECIST criteria. (vii) Adequate organ function, defined as absolute neutrophil count >1500/μL, platelet count >100,000/μl, and levels of creatinine, liver enzymes, and alanine aminotransferase (ALT) less than two times the upper limits of normal (ULN). (viii) Written informed consent was obtained.
Fourier Transform and Autoregressive HRV Features in Prediction and Classification of Breast Cancer
Published in IETE Journal of Research, 2023
Reema Shyamsunder Shukla, Yogender Aggarwal
Breast cancer (BC) is known to be the highest degree non-skin malignancy in women and the second principal reason of female cancer death. The malignancy of the BC develops from breast cells and tissues. In developed countries, it has evolved as a primary reason of death [7,8]. Literature review suggested that various machine learning techniques have been applied to detect breast cancer like logistic regression, linear discriminate analysis, naïve Bayes, decision trees, artificial neural network (ANN), k-nearest neighbour, random forest, support vector machine (SVM) and SVM Ensemble methods [9–11]. In prediction analysis of BC, different algorithms of ANNs were implemented, which revealed an accuracy of 96.18% for radial basis function, 97% for probabilistic neural network, 98.8% for generalised regression neural network and 95.74% for multilayer perceptron neural network. The architecture used was nine input nodes of different attributes like external appearance changes and internal chromosomal changes of breast cancer and two output classes of benign and malignant with learning rate of 0.6 [12]. In one of the studies using SVM, 10 different features have been extracted from mammograms as input features to classify into 2 output classes of benign and malignant giving a sensitivity of 88.75% [13]. Along with accuracy other evaluation metrics like sensitivity (detects benign lesion accurately), specificity (detects malignant tumour accurately), precision, F-measure and area under the curve (AUC) (more than 50% presented appropriate tumour classification) have also been used [10]. Previous research works revealed the successful application of HRV spectral features in the identification of performance status (PS) in pulmonary metastasis and lung cancer using Eastern Cooperative Oncology (ECOG) Scale [14,15].
Malignant pleural mesothelioma: Presentation of a case report
Published in Egyptian Journal of Basic and Applied Sciences, 2018
Munir Ahmad, Muhammad Omer Aamir, Khurram Minhas, Khwaja Ajmal, Iftikhar Ahmad
A 56-year-old female presented to us complaining of chest pain, cough, shortness of breath, weight loss and headache. Hematology analysis revealed marked decrease in platelets (i.e., 94,000/mm3). The patient performance status on the scale of Eastern Cooperative Oncology Group (ECOG) was one. Initial chest x-ray of the patient showed right pleural thickening and obliteration of cardio-phrenic and costo-phrenic angles. Additional radiological work-up, i.e., Computed Tomography (CT) study of chest revealed right sided plural effusion with circumferential plural thickening and loss of lung volume, as shown in Fig. 1A. Soft tissue density area in retro-areolar region of left breast was also observed. Surgery (radical or pleurectomy) was not possible due to high tumor burden. The patient remained at home without any management for 03 months. Thereafter, CT-guided plural incisional biopsy (specimen size = 2.5 × 2 × 1 cm) revealed infiltrating neoplastic lesion composed of sheets, nests and cluster of cells with focal glandular pattern having cells with round to polygonal morphology and having moderate to abundant eosinophilic cytoplasm and hyperchromatic, pleomorphic nuclei with prominent nucleoli. Scattered multinucleated cells were also identified. Special stain PAS/AB highlighted glycogen in the tumor cells. IHC staining showed the following patterns: calretinin, cytokeratin 5/6, cytokeratin CAM 5.2, Wilms tumour antigen-1 (WT-1) and cytokeratin 7 were positive while cytokeratin 20, CDX2, MR and P63 were negative. All these morphologic and IHC features (Fig. 2), in tandem with radiological findings, (summarized in Table 1) were indicative of MPM. Additional analysis showed that other biochemical measures were within normal limits. Pelvic ultrasound also demonstrated no abnormality.