Classification of oligometastatic prostate cancer with additional consideration for hormone sensitivity

Abstract Background Due to improved imaging sensitivity, the term “oligometastatic” prostate cancer disease is diagnosed more often, leading to an increasing interest in metastasis-directed therapy (MDT). There are two types of radiation based MDT applied when treating oligometastatic disease: (1) stereotactic body radiation therapy (SBRT) generally used for bone metastases; or (2) SBRT for isolated nodal oligometastases combined with prophylactic elective nodal radiotherapy. This review aims to summarize current evidence data, which may shed light on the optimal management of this heterogeneous group of patients. Methods A systematic review of the Medline database through PubMed was performed according to PRISMA guidelines. All relevant studies published up to November 2020 were identified and screened. Fifty-six titles were included. Besides outcome parameters, different prognostic and predictive factors were assessed, including site of metastases, time between primary treatment and MDT, use of systemic therapies, hormone sensitivity, as well as pattern of recurrence. Findings Evidence consists largely of retrospective case series and no consistent precise definition of oligometastasis exists, however, most investigators seem to acknowledge the need to distinguish between patients presenting with what is frequently called “synchronous” versus “metachronous” oligometastatic disease. Available data on radiotherapy as MDT demonstrate high local control rates and a small but relevant proportion of patients without progressive disease after 2 years. This holds true for both hormone sensitive and castration resistant prostate cancer diseases. The use of 68Ga-PSMA PET/CT for staging increased dramatically. Radiation doses and field sizes varied considerably among the studies. The search for relevant prognostic and predictive factors is ongoing. Conclusions To our best knowledge this review on oligometastatic prostate cancer included the largest number of original articles. It demonstrates the therapeutic potential and challenges of MDT for oligometastatic prostate cancer. Prospective studies are under way and will provide further high-level evidence.

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Survey of denoising, segmentation and classification of magnetic resonance imaging for prostate cancer

Multimedia Tools and Applications ◽

10.1007/s11042-021-11044-2 ◽

2021 ◽

Author(s):

Mamta Juneja ◽

Sumindar Kaur Saini ◽

Jatin Gupta ◽

Poojita Garg ◽

Niharika Thakur ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Prostate Cancer ◽

Magnetic Resonance ◽

Resonance Imaging

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Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

Applied Sciences ◽

10.3390/app11093836 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3836

Author(s):

Valeri Gitis ◽

Alexander Derendyaev ◽

Konstantin Petrov ◽

Eugene Yurkov ◽

Sergey Pirogov ◽

...

Keyword(s):

Prostate Cancer ◽

Learning Algorithm ◽

Binary Classification ◽

Preoperative Staging ◽

Threshold Value ◽

Logical Function ◽

Adequate Treatment ◽

Monotonic Functions ◽

Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

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