Circulating tumor cells as prognostic marker in pancreatic cancer

In this editorial we comment on the article by Zhang et al published in the recent issue of the World Journal of Clinical Oncology. Pancreatic cancer is the fourth most common cause of cancer-related mortality and has the lowest survival rate among all solid cancers. It causes 227000 deaths annually worldwide, and the 5-year survival rate is very low due to early metastasis, which is 4.6%. Cancer survival increases with better knowledge of risk factors and early and accurate diagnosis. Circulating tumor cells (CTCs) are tumor cells that intravasate from the primary tumor or metastasis foci into the peripheral blood circulation system spontaneously or during surgical operations. Detection of CTC in blood is promising for early diagnosis. In addition, studies have associated high CTC levels with a more advanced stage, and more intensive treatments should be considered in cases with high CTC. In tumors that are considered radiologically resectable, it may be of critical importance in detecting occult metastases and preventing unnecessary surgeries.

Prognostic factors of breast cancer brain metastasis

In this editorial we comment on the article by Chen et al published in the recent issue of the World Journal of Clinical Oncology. Brain metastasis is one of the most serious complications of breast cancer and causes high morbidity and mortality. Brain metastases may involve the brain parenchyma and/or leptomeninges. Symptomatic brain metastases develop in 10%-16% of newly recognized cases each year, and this rate increases to 30% in autopsy series. Depending on the size of the metastatic foci, it may be accompanied by extensive vasogenic edema or may occur as small tumor foci. Since brain metastases are a significant cause of morbidity and mortality, early diagnosis can have significant effects on survival and quality of life. The risk of developing brain metastases emerges progressively due to various patient and tumor characteristics. Patient variability may be particularly important in the susceptibility and distribution of brain metastases because malignant blood must cross the brain barrier and move within the brain parenchyma. Some characteristics of the tumor, such as gene expression, may increase the risk of brain metastasis. Clinical growth, tumor stage, tumor grade, growth receptor positivity, HER2 positivity, molecular subtype (such as triple negative status, luminal/nonluminal feature) increase the risk of developing breast cancer metastasis. Factors related to survival due to breast cancer brain metastasis include both tumor/patient characteristics and treatment characteristics, such as patient age, lung metastasis, surgery for brain metastasis, and HER2 positivity. If cases with a high risk of developing brain metastasis can be identified with the help of clinical procedures and artificial intelligence, survival and quality of life can be increased with early diagnosis and treatment. At the same time, it is important to predict the formation of this group in order to develop new treatment methods in cases with low survival expectancy with brain metastases.

Lateral lymph node involvement presents another challenge in rectum cancer in the age of neoadjuvant chemoradiotherapy + total mesorectal excision: an evaluation of survival outcomes

PURPOSE

In the last decades, total mesorectal excision (TME) and neoadjuvant chemoradiotherapy (nCRT) have produced an undeniable improvement in the treatment of rectal cancer. However, local recurrence is still an important problem, and the effect of lateral lymph node (LLN) involvement on local recurrence is a controversial issue. The aim of this study was to investigate the effects of LLN status on local recurrence and survival in rectal cancers treated with nCRT + TME.

METHODS

Clinical features, pre- and post-nCRT lateral pelvic region imaging, long-term local recurrence, and the survival outcomes of 114 patients who underwent nCRT + TME for rectal cancer were evaluated.

RESULTS

On MRI before nCRT, 20 (17.5%) patients had lateral lymph nodes (LLN+), and 94 (82.5%) patients had no lymph nodes in the lateral pelvic compartments (LLN-). Local recurrences at 1 year in LLN+ and LLN- patients were 3 (15.8%) and 2 (2.3%), respectively (p=0.039). Five-year local recurrence-free survival rates and the mean duration of recurrence-free survival in LLN+ and LLN- patients were 56.2%, 42.6 months, and 87.3% 66.9 months, respectively (p=0.001). Disease-free survival and overall survival were shorter in LLN+ patients, but the difference was not statistically significant (p=0.096 and p=0.46, respectively). In the multivariate analysis, LLN involvement was determined to be an independent risk factor for local recurrence-free survival (Hazard Ratio 4.54, p=0.003).

CONCLUSION

Lateral lymph node involvement causes local recurrence to remain high after nCRT + TME. LLN status should be considered in treatment planning. Further studies are needed to define precise criteria for LLN involvement and the effect of LLND on local recurrence and survival.

Artificial intelligence in rectal cancer

Accurate and rapid diagnosis is essential for correct treatment in rectal cancer. Determining the optimal treatment plan for a patient with rectal cancer is a complex process, and the oncological results and toxicity are not the same in every patient with the same treatment at the same stage. In recent years, the increasing interest in artificial intelligence in all fields of science has also led to the development of innovative tools in oncology. Artificial intelligence studies have increased in many steps from diagnosis to follow-up in rectal cancer. It is thought that artificial intelligence will provide convenience in many ways from personalized treatment to reducing the workload of the physician. Prediction algorithms can be standardized by sharing data between centers, diversifying data, and creating big data.

Artificial intelligence in radiation oncology

Artificial intelligence (AI) is a computer science that tries to mimic human-like intelligence in machines that use computer software and algorithms to perform specific tasks without direct human input. Machine learning (ML) is a subunit of AI that uses data-driven algorithms that learn to imitate human behavior based on a previous example or experience. Deep learning is an ML technique that uses deep neural networks to create a model. The growth and sharing of data, increasing computing power, and developments in AI have initiated a transformation in healthcare. Advances in radiation oncology have produced a significant amount of data that must be integrated with computed tomography imaging, dosimetry, and imaging performed before each fraction. Of the many algorithms used in radiation oncology, has advantages and limitations with different computational power requirements. The aim of this review is to summarize the radiotherapy (RT) process in workflow order by identifying specific areas in which quality and efficiency can be improved by ML. The RT stage is divided into seven stages: patient evaluation, simulation, contouring, planning, quality control, treatment application, and patient follow-up. A systematic evaluation of the applicability, limitations, and advantages of AI algorithms has been done for each stage.

Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study

Background:

Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient and tumor characteristics, dosimetric parameters, and treatment features are intertwined, and it is not always possible to associate RP with a single parameter. This study aimed to determine the algorithm that most accurately predicted RP development with machine learning.

Methods:

Of the 197 cases diagnosed with stage III lung cancer and underwent RT and chemotherapy between 2014 and 2020, 193 were evaluated. The CTCAE 5.0 grading system was used for the RP evaluation. Synthetic minority oversampling technique was used to create a balanced data set. Logistic regression, artificial neural networks, eXtreme Gradient Boosting (XGB), Support Vector Machines, Random Forest, Gaussian Naive Bayes and Light Gradient Boosting Machine algorithms were used. After the correlation analysis, a permutation-based method was utilized for as a variable selection.

Results:

RP was seen in 51 of the 193 cases. Parameters affecting RP were determined as, total(t)V5, ipsilateral lung D_max, contralateral lung D_max, total lung D_max, gross tumor volume, number of chemotherapy cycles before RT, tumor size, lymph node localization and asbestos exposure. LGBM was found to be the algorithm that best predicted RP at 85% accuracy (confidence interval: 0.73-0.96), 97% sensitivity, and 50% specificity.

Conclusion:

When the clinical and dosimetric parameters were evaluated together, the LGBM algorithm had the highest accuracy in predicting RP. However, in order to use this algorithm in clinical practice, it is necessary to increase data diversity and the number of patients by sharing data between centers.