Comparison of Prognostic Indices in NSCLC Patients with Brain Metastases after Radiosurgery

Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation

Early diagnosis and accurate prognosis play a pivotal role in the clinical management of cancer and in preventing cancer-related mortalities. The burgeoning population of Asia in general and South Asian countries like India in particular pose significant challenges to the healthcare system. Regrettably, the demand for healthcare services in India far exceeds the available resources, resulting in overcrowded hospitals, prolonged wait times, and inadequate facilities. The scarcity of trained manpower in rural settings, lack of awareness and low penetrance of screening programs further compounded the problem. Artificial Intelligence (AI), driven by advancements in machine learning, deep learning, and natural language processing, can profoundly transform the underlying shortcomings in the healthcare industry, more for populous nations like India. With about 1.4 million cancer cases reported annually and 0.9 million deaths, India has a significant cancer burden that surpassed several nations. Further, India's diverse and large ethnic population is a data goldmine for healthcare research. Under these circumstances, AI-assisted technology, coupled with digital health solutions, could support effective oncology care and reduce the economic burden of GDP loss in terms of years of potential productive life lost (YPPLL) due to India's stupendous cancer burden. This review explores different aspects of cancer management, such as prevention, diagnosis, precision treatment, prognosis, and drug discovery, where AI has demonstrated promising clinical results. By harnessing the capabilities of AI in oncology research, healthcare professionals can enhance their ability to diagnose cancers at earlier stages, leading to more effective treatments and improved patient outcomes. With continued research and development, AI and digital health can play a transformative role in mitigating the challenges posed by the growing population and advancing the fight against cancer in India. Moreover, AI-driven technologies can assist in tailoring personalized treatment plans, optimizing therapeutic strategies, and supporting oncologists in making well-informed decisions. However, it is essential to ensure responsible implementation and address potential ethical and privacy concerns associated with using AI in healthcare.

Enhancing outcomes: neurosurgical resection in brain metastasis patients with poor Karnofsky performance score - a comprehensive survival analysis

Background

A reduced Karnofsky performance score (KPS) often leads to the discontinuation of surgical and adjuvant therapy, owing to a lack of evidence of survival and quality of life benefits. This study aimed to examine the clinical and treatment outcomes of patients with KPS < 70 after neurosurgical resection and identify prognostic factors associated with better survival.

Methods

Patients with a preoperative KPS < 70 who underwent surgical resection for newly diagnosed brain metastases (BM) between 2007 and 2020 were retrospectively analyzed. The KPS, age, sex, tumor localization, cumulative tumor volume, number of lesions, extent of resection, prognostic assessment scores, adjuvant radiotherapy and systemic therapy, and presence of disease progression were analyzed. Univariate and multivariate logistic regression analyses were performed to determine the factors associated with better survival. Survival > 3 months was considered favorable and ≤ 3 months as poor.

Results

A total of 140 patients were identified. Median overall survival was 5.6 months (range 0-58). There was no difference in the preoperative KPS between the groups of > 3 and ≤ 3 months (50; range, 20-60 vs. 50; range, 10-60, p = 0.077). There was a significant improvement in KPS after surgery in patients with a preoperative KPS of 20% (20 vs 40 ± 20, p = 0.048). In the other groups, no significant changes in KPS were observed. Adjuvant radiotherapy was associated with better survival (44 [84.6%] vs. 32 [36.4%]; hazard ratio [HR], 0.0363; confidence interval [CI], 0.197-0.670, p = 0.00199). Adjuvant chemotherapy and immunotherapy resulted in prolonged survival (24 [46.2%] vs. 12 [13.6%]; HR 0.474, CI 0.263-0.854, p = 0.013]. Systemic disease progression was associated with poor survival (36 [50%] vs. 71 [80.7%]; HR 5.975, CI 2.610-13.677, p < 0.001].

Conclusion

Neurosurgical resection is an appropriate treatment modality for patients with low KPS. Surgery may improve functional status and facilitate further tumor-specific treatment. Combined treatment with adjuvant radiotherapy and systemic therapy was associated with improved survival in this cohort of patients. Systemic tumor progression has been identified as an independent factor for a poor prognosis. There is almost no information regarding surgical and adjuvant treatment in patients with low KPS. Our paper provides novel data on clinical outcome and survival analysis of patients with BM who underwent surgical treatment.

A 3-month survival model after Gamma Knife surgery in patients with brain metastasis from lung cancer with Karnofsky performance status ≤ 70

Gamma Knife surgery (GKS) for brain metastasis (BM) has been generally advocated for patients with a Karnofsky performance status (KPS) scale of ≥ 70. However, some patients with a poor KPS scale of < 70 are recoverable after GKS and show durable survival. A purpose of this study is to devise a 3-month survival prediction model to screen patients with BM with a KPS of ≤ 70 in whom GKS is needed. A retrospective analysis of 67 patients with a KPS scale of 60-70 undergoing GKS for BM of non-small cell lung cancer (NSCLC) from 2016 to 2020 in our institute was performed. Univariate and multivariate logistic regression analyses were performed to investigate factors related to survival for more than 3 months after GKS. The probability (P) prediction model was designed by giving a weight corresponding to the odds ratio of the variables. The overall survival was 9.9 ± 12.7 months (range 0.2-53.2), with a 3-month survival rate of 59.7% (n = 40). In multivariate logistic regression analysis, extracranial disease (ECD) control (p = .033), focal neurological deficit (FND) (p = .014), and cumulative tumor volume (∑ TV) (p = .005) were associated with 3-month survival. The prediction model of 3-month survival (Harrell's C index = 0.767) was devised based on associated factors. In conclusion, GKS for BMs is recommended in selected patients, even if the KPS scale is ≤ 70.

Establishment of a prognostic risk prediction modelfor non-small cell lung cancer patients with brainmetastases: a retrospective study

Background

Patients with non-small cell lung cancer (NSCLC) who develop brain metastases (BM) have a poor prognosis. This study aimed to construct a clinical prediction model to determine the overall survival (OS) of NSCLC patients with BM.

Methods

A total of 300 NSCLC patients with BM at the Yunnan Cancer Centre were retrospectively analysed. The prediction model was constructed using the least absolute shrinkage and selection operator-Cox regression. The bootstrap sampling method was employed for internal validation. The performance of our prediction model was compared using recursive partitioning analysis (RPA), graded prognostic assessment (GPA), the update of the graded prognostic assessment for lung cancer using molecular markers (Lung-molGPA), the basic score for BM (BSBM), and tumour-lymph node-metastasis (TNM) staging.

Results

The prediction models comprising 15 predictors were constructed. The area under the curve (AUC) values for the 1-year, 3-year, and 5-year time-dependent receiver operating characteristic (curves) were 0.746 (0.678-0.814), 0.819 (0.761-0.877), and 0.865 (0.774-0.957), respectively. The bootstrap-corrected AUC values and Brier scores for the prediction model were 0.811 (0.638-0.950) and 0.123 (0.066-0.188), respectively. The time-dependent C-index indicated that our model exhibited significantly greater discrimination compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging. Similarly, the decision curve analysis demonstrated that our model displayed the widest range of thresholds and yielded the highest net benefit. Furthermore, the net reclassification improvement and integrated discrimination improvement analyses confirmed the enhanced predictive power of our prediction model. Finally, the risk subgroups identified by our prognostic model exhibited superior differentiation of patients' OS.

Conclusion

The clinical prediction model constructed by us shows promise in predicting OS for NSCLC patients with BM. Its predictability is superior compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging.

Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective

Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essential to improve the patient's survival rate. With artificial intelligence (AI) approaches widely applied in lung cancer, early diagnosis and prediction have achieved excellent performance in recent years. This review summarizes various types of AI algorithm applications in lung cancer, including natural language processing (NLP), machine learning and deep learning, and reinforcement learning. In addition, we provides evidence regarding the application of AI in lung cancer diagnostic and clinical prognosis. This review aims to elucidate the value of AI in lung cancer diagnosis and prognosis as the novel screening decision-making for the precise treatment of lung cancer patients.

Role of Artificial Intelligence and Machine Learning in Prediction, Diagnosis, and Prognosis of Cancer

Cancer is one of the most devastating, fatal, dangerous, and unpredictable ailments. To reduce the risk of fatality in this disease, we need some ways to predict the disease, diagnose it faster and precisely, and predict the prognosis accurately. The incorporation of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms into the healthcare system has already proven to work wonders for patients. Artificial intelligence is a simulation of intelligence that uses data, rules, and information programmed in it to make predictions. The science of machine learning (ML) uses data to enhance performance in a variety of activities and tasks. A bigger family of machine learning techniques built on artificial neural networks and representation learning is deep learning (DL). To clarify, we require AI, ML, and DL to predict cancer risk, survival chances, cancer recurrence, cancer diagnosis, and cancer prognosis. All of these are required to improve patient's quality of life, increase their survival rates, decrease anxiety and fear to some extent, and make a proper personalized treatment plan for the suffering patient. The survival rates of people with diffuse large B-cell lymphoma (DLBCL) can be forecasted. Both solid and non-solid tumors can be diagnosed precisely with the help of AI and ML algorithms. The prognosis of the disease can also be forecasted with AI and its approaches like deep learning. This improvement in cancer care is a turning point in advanced healthcare and will deeply impact patient's life for good.

Comprehensive summary and retrospective evaluation of prognostic scores for patients with newly diagnosed brain metastases treated with upfront radiosurgery in a modern patient collective

BACKGROUND

Numerous prognostic scores (PS) for patients with brain metastases (BM) have been developed. Recently, PS based on laboratory parameters were introduced to better predict overall survival (OS). A comprehensive comparison of the wide range of scores in a modern patient collective is still missing.

MATERIALS AND METHODS

Twelve PS considering clinical parameters only at the time of BM diagnosis were calculated for 470 patients receiving upfront SRS between January 2014 and March 2020. In a subcohort of 310 patients where a full laboratory dataset was available five additional prognostic scores were compared. Restricted mean survival time (RMST), partial likelihood and c-index were calculated as metrics for performance evaluation. Univariable and multivariable analysis were used to identify prognostic factors for OS.

RESULTS

The median OS of the whole cohort was 15.8 months (95% C.I.: 13.4-20.1). All prognostic scores performed well in separating patients into different prognostic groups. RPA achieved the highest c-index, whereas GGS achieved highest partial likelihood with evaluation in the total cohort. With incorporation of the laboratory scores the recently suggested EC-GPA achieved highest c-index and highest partial likelihood. A prognostic score solely based on the assessment of performance status achieved considerable high performance as either 3- or 4-tiered score. Multivariable analysis revealed performance status, systemic disease status and laboratory parameters to be significantly associated with OS among variates included in prognostic scores.

CONCLUSION

Although recent PS incorporating laboratory parameters show convincing performance in predicting overall survival, older scores relying on clinical parameters only are still valid and appealing as they are easier to calculate, and as overall performance is almost equal. Moreover, a score just based on performance status is not significantly inferior and should at least be assessed for informed decision making.

Prognostic nutritional index and systemic immune-inflammation index are prognostic biomarkers for non-small-cell lung cancer brain metastases

Aim: This research aimed to elucidate the prognosis values of prognostic nutritional index (PNI) and systemic immune-inflammation index (SII) and clinical characteristics in NSCLC patients with brain metastases (BM) underwent radiotherapy. Materials & methods: Cut-off points of hematological indicators were determined by receiver operating characteristic curve. Overall survival was evaluated by Kaplan-Meier method and Cox proportional hazards model. Results: We retrospectively analyzed 214 patients from January 2009 to December 2018. The result demonstrated the independent prognostic values of PNI (hazard ratio: 0.600; p = 0.004) and SII (hazard ratio: 1.486; p = 0.019). The remaining clinicopathologic factors, including brain radiotherapy modality, smoking history, numbers of brain metastases, intracranial symptoms and Radiation Therapy Oncology Group - recursive partitioning analysis, were independently related to survival (p < 0.05). Conclusion: PNI and SII could be critical prognostic indicators for NSCLC patients with BM.

Nanomedicine-Combined Immunotherapy for Cancer

BACKGROUND

Immunotherapy for cancer includes Chimeric Antigen Receptor (CAR)-T cells, CAR-natural Killer (NK) cells, PD1, and the PD-L1 inhibitor. However, the proportion of patients who respond to cancer immunotherapy is not satisfactory. Concurrently, nanotechnology has experienced a revolution in cancer diagnosis and therapy. There are few clinically approved nanoparticles that can selectively bind and target cancer cells and incorporate molecules, although many therapeutic nanocarriers have been approved for clinical use. There are no systematic reviews outlining how nanomedicine and immunotherapy are used in combination to treat cancer.

OBJECTIVE

This review aims to illustrate how nanomedicine and immunotherapy can be used for cancer treatment to overcome the limitations of the low proportion of patients who respond to cancer immunotherapy and the rarity of nanomaterials in clinical use.

METHODS

A literature review of MEDLINE, PubMed / PubMed Central, and Google Scholar was performed. We performed a structured search of literature reviews on nanoparticle drug-delivery systems, which included photodynamic therapy, photothermal therapy, photoacoustic therapy, and immunotherapy for cancer. Moreover, we detailed the advantages and disadvantages of the various nanoparticles incorporated with molecules to discuss the challenges and solutions associated with cancer treatment.

CONCLUSION

This review identified the advantages and disadvantages associated with improving health care and outcomes. The findings of this review confirmed the importance of nanomedicinecombined immunotherapy for improving the efficacy of cancer treatment. It may become a new way to develop novel cancer therapeutics using nanomaterials to achieve synergistic anticancer immunity.

Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model

This study is to establish the nomogram model and provide clinical therapy decision-making for extensive-stage small-cell lung cancer (ES-SCLC) patients with different metastatic sites using the Surveillance, Epidemiology, and End Results (SEER) Program.A total of 10,025 patients of ES-SCLC with metastasis from January 2010 to December 2016 were enrolled from the SEER database. All samples were randomly divided into a derivation cohort and a validation cohort, and the derivation cohort was divided into 6 groups by different metastatic sites: bone, liver, lung, brain, multiple organs, and other organs. Using Cox proportional hazards models to analyze candidate prognostic factors, screening out the independent prognostic factors to establish the nomogram. Compare the different models by Net reclassification improvement and integrated discrimination improvement. Concordance index (C-index) and the calibration curve were used to verify the prediction efficiency of the nomogram in the derivation cohort and validation cohort.In the derivation cohort, the median overall survival was 7 months. The overall survival rates at 6-month, 1-year, and 2-year were 55.07%, 24.61%, and 7.56%, respectively. The median survival time was 10, 8, 7, 9, 7, and 6 months for the 6 groups of different metastatic sites: other, bone, liver, lung, brain, and multiple organs, respectively. Age, sex, race, T, N, distant metastatic site, and chemotherapy were contained in the final nomogram prognostic model. The C-index was 0.6569777 in the derivation cohort and 0.8386301 in the validation cohort.The survival time of ES-SCLC patients with different metastatic sites was significantly different. The nomogram can effectively predict the prognosis of individuals and provide a basis for clinical decision-making.

Validation of recursive partitioning analysis, graded prognostic assessment and basic score for brain metastases as prognostic indices among patients with brain metastases treated with radiotherapy in Indonesia

Introduction:

Metastatic brain disease is still a major contributor to cancer treatment failure. Various treatments have improved in the recent decades, which allow for better control of brain metastatic lesions. Various prognostic scoring tools have been developed and used worldwide to stratify patients with brain metastases to determine who will benefit most from aggressive treatment. The three most commonly used prognostic scoring tools are recursive partitioning analysis (RPA), basic score for brain metastases (BSBM) and graded prognostic assessment (GPA). The aim of this study is to validate these scoring tools using an Indonesian cancer patient population.

Method:

A retrospective analysis of all patients presenting with brain metastases from January 2012 until December 2014, through using hospital medical records, was conducted. All patients receiving whole brain radiotherapy during this period were included in this study. A follow-up with a telephone call was carried out to determine the patient’s health and survival status. Uncontactable patients were excluded from the analysis. Survival analysis was carried out by stratifying patients based on the three prognostic scoring systems.

Result:

A total of 80 patients were eligible to be included in the study, with 18 excluded due to being uncontactable. The remaining 62 patients’ data were analysed and stratified with all three scoring systems. The RPA was found to confer better stratification than BSBM and GPA in our study population.

Conclusion:

GPA was non-prognostic in our study population and BSBM was less prognostic, especially in the middle group, class 1 and class 2. Those BSBM class 1 and class 2 did not provide good prognostic stratification in our study population, whereas RPA was proven to be the best in stratifying patients’ prognosis with brain metastases in our study population.

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.

Mining Prognosis Index of Brain Metastases Using Artificial Intelligence

This study is to identify the optimum prognosis index for brain metastases by machine learning. Seven hundred cancer patients with brain metastases were enrolled and divided into 446 training and 254 testing cohorts. Seven features and seven prediction methods were selected to evaluate the performance of cancer prognosis for each patient. We used mutual information and rough set with particle swarm optimization (MIRSPSO) methods to predict patient’s prognosis with the highest accuracy at area under the curve (AUC) = 0.978 ± 0.06. The improvement by MIRSPSO in terms of AUC was at 1.72%, 1.29%, and 1.83% higher than that of the traditional statistical method, sequential feature selection (SFS), mutual information with particle swarm optimization(MIPSO), and mutual information with sequential feature selection (MISFS), respectively. Furthermore, the clinical performance of the best prognosis was superior to conventional statistic method in accuracy, sensitivity, and specificity. In conclusion, identifying optimal machine-learning methods for the prediction of overall survival in brain metastases is essential for clinical applications. The accuracy rate by machine-learning is far higher than that of conventional statistic methods.

Brain Metastases from Lung Cancer: Is MET an Actionable Target?

The process of metastatic dissemination begins when malignant cells start to migrate and leave the primary mass. It is now known that neoplastic progression is associated with a combination of genetic and epigenetic events. Cancer is a genetic disease and this pathogenic concept is the basis for a new classification of tumours, based precisely on the presence of definite genetic lesions to which the clones are addicted. Regarding the scatter factor receptors MET and Recepteur d'Origin Nantais (RON), it is recognised that MET is an oncogene necessary for a narrow subset of tumours (MET-addicted) while it works as an adjuvant metastogene for many others. This notion highlights that the anti-MET therapy can be effective as the first line of intervention in only a few MET-addicted cases, while it is certainly more relevant to block MET in cases of advanced neoplasia that exploit the activation of the invasive growth program to promote dissemination in other body parts. Few data are instead related to the role played by RON, a receptor homologous to MET. We have already demonstrated an implication of MET and RON genes in brain metastases from lung cancer. On this basis, the aim of this work is to recapitulate and dissect the molecular basis of metastatic brain dissemination from lung cancer. The latter is among the big killers and frequently gives rise to brain metastases, most often discovered at diagnosis. Molecular mechanisms leading to tumour spread to the brain are mostly unknown and in turn these tragic cases are still lacking effective therapies. Based on previously published data from our group, we aim to summarise and analyse the pathogenic mechanisms leading to activation of the scatter factor receptor in brain metastatic lesions of lung primaries, from the point of view of replacing the currently used empirical treatment with a more targeted approach.